JP7595885B2 - NETWORK ANALYSIS METHOD, NETWORK ANALYSIS SYSTEM AND SERVER - Google Patents

Description

The present invention relates to a technique for analyzing population networks.

With advances in sensing devices and communication technology, technology that analyzes group behavior has been attracting attention in the fields of sports, business, and education. There are many known cases in which the results of group analysis have been used to activate groups.

In group analysis, groups that communicate are broadly defined by considering not only conscious communication between individuals, such as face-to-face meetings and conversations, but also unconscious communication, such as the synchronization of body movements between individuals. It is also known that the state of communication within a group can be expressed using a network diagram.

In the field of sports, a technology for analyzing networks within a group is known as Patent Document 1, which analyzes the relationships between individuals during a competition. In the field of business, a technology for extracting communities within an organization is known as Patent Document 2.

Patent Document 1 discloses a technique for extracting individuals that act in sync with a group and visualizing the group network. Patent Document 2 discloses a technique for extracting communities within an organization and measuring and visualizing the quality of the extracted communities.

JP 2018-81406 A JP 2012-27589 A

The time series changes in a group network are extremely complex, making it difficult to easily grasp structural changes in the network between individuals. In other words, the above conventional technology can calculate the relationships and activity levels between individuals at a certain time, and can grasp the extent of the relationships between individuals. However, the above conventional technology has the problem that it cannot easily represent changes in the activity levels and diversity of communication within a group.

The present invention was made in consideration of the above problems, and aims to visualize the activity level and changes in network structure of groups that are engaged in purposeful activities.

The present invention is a network analysis method in which a computer including a processor and a memory analyzes a network between individuals in a population, and includes a sensing data collection step in which the computer collects time-series information on the activities of individuals constituting the population and stores the collected information in sensor data, a network analysis step in which the computer extracts activated individuals from the sensor data as cliques and generates a network composed of the cliques in time series, and a group consciousness analysis step in which the computer calculates the occurrence probability of the cliques and the information entropy of the cliques to calculate an index indicating the activity level of the population and the diversity of the population as a group consciousness index.

Therefore, by quantifying indicators that indicate the activity level and diversity of a group, the present invention makes it possible to visualize the changes over time in the network structure of a group that is acting with a purpose, and makes it easy to understand the consciousness of the group.

Details of at least one implementation of the subject matter disclosed herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the disclosed subject matter will become apparent from the following disclosure, drawings, and claims.

FIG. 1 illustrates a first embodiment of the present invention, showing an example of a network analysis system applied to the field of sports. 4 is a flowchart illustrating an outline of a process performed by the server according to the first embodiment of the present invention. FIG. 4 illustrates an example of sensor data according to the first embodiment of the present invention. FIG. 10 illustrates an example of feature amount information according to the first embodiment of the present invention. 11 is a flowchart illustrating an example of processing performed by the network analysis unit according to the first embodiment of the present invention. FIG. 2 illustrates an example of synchronous network time series data according to the first embodiment of the present invention. FIG. 1 illustrates a first embodiment of the present invention and is a diagram illustrating an example of a relationship between sensor data and synchronous network time-series data. FIG. 1 illustrates a first embodiment of the present invention and shows an example of extracting a clique from synchronous network time series data. FIG. 13 illustrates an example of occurrence probability data according to the type of clique according to the first embodiment of the present invention. 10 is a flowchart illustrating an example of processing performed by the collective consciousness analysis unit according to the first embodiment of the present invention. FIG. 1 shows a plot diagram illustrating the area of collective consciousness according to a first embodiment of the present invention. FIG. 1 shows Example 1 of the present invention and is a plot diagram showing trends in the consciousness of a group. FIG. 1 shows Example 1 of the present invention and is a plot diagram of three cliques analyzed in a population of athletic fields. FIG. 11 illustrates a second embodiment of the present invention, and is a diagram illustrating an example of a network analysis system applied to the field of education. FIG. 11 illustrates a second embodiment of the present invention, and is a diagram illustrating an example of the relationship between the meeting matrix at time T1 and the communication network time series data. FIG. 11 illustrates a second embodiment of the present invention, and is a diagram showing an example of the relationship between the meeting matrix at time T2 and the communication network time series data. FIG. 13 is a plot diagram of one clique analyzed using a population in the education field, illustrating Example 2 of the present invention.

The following describes an embodiment of the present invention with reference to the attached drawings.

Figure 1 shows an example of a network analysis system applied to group sports. The network analysis system of Example 1 is a computer system that analyzes relationships based on the movements of individuals (players) 60 that make up a group, extracts networks (connections between individuals), generates time-series data, and visualizes changes in the activity and diversity (structure) of the group.

Note that, although the following describes a computer system that visualizes human networks, the system can be applied to any system that analyzes and visualizes networks between moving individuals, such as animals.

The network visualization system includes a sensing device 1, a network 4, and a server 5.

An example of a group that is the subject of analysis by the network analysis system in Example 1 is a group that plays soccer. The type of exercise may be any sport in which two or more individuals participate, and the network analysis system in Example 1 may be applied to group sports such as rugby, basketball, volleyball, goalball, baseball, tennis, handball, lacrosse, track and field, and speed skating.

The network analysis system of Example 1 is not limited to networks between individuals, and may be used to display networks between human bodies and objects, or between objects, by measuring the movements of objects such as balls, bats, rackets, and other tools and implements with the sensing device 1.

In Example 1, one or more sensing devices 1 are attached to the body of an individual 60 performing exercise. The individual 60 in Example 1 shows an example in which a wristwatch-type wearable device equipped with a three-axis acceleration sensor is attached directly to the human body as the sensing device 1. The individual 60 performs exercise on a field, but the location where the exercise is performed is not limited to a field and may be any location.

In addition, by specifying the location where the exercise will be performed, even if an individual wearing sensing device 1 is in multiple locations, it is possible to display a network of only individuals in the specified location. In this embodiment, individual 60 is an individual that is displayed in the network and is also an individual that is measured by sensing device 1.

The sensing device 1 in this embodiment is equipped with at least one of various sensors that measure the activity of an individual. The various sensors include, but are not limited to, an acceleration sensor, a gyro sensor, a pedometer, a heart rate monitor, a position sensor, an impact sensor, a magnetometer, a temperature sensor, a humidity sensor, a wind sensor, a sound sensor, a barometric pressure sensor, a positioning sensor, an infrared sensor, and a position information sensor. The results of measurements by these various sensors may be transmitted to the server 5 as sensing data.

The individual 60 may also wear the sensing device 1 so that it is physically connected to a body part of the individual 60, such as the head, neck, shoulders, back, arms, wrists, hands, fingers, waist, hips, legs, ankles, feet, heels, and toes. If there is one or more pieces of clothing, footwear, or athletic protective gear between the sensing device 1 and the body of the individual 60, the individual 60 may wear the sensing device 1 by various removable or non-removable connecting means such as straps, adhesives, pockets, and clips, in a state where the sensing device 1 is integrated with the clothing, footwear, or athletic protective gear used for the athletic activity. The sensing device 1 may also be attached to or embedded in an object such as an implement or tool, such as a ball, bat, or racket.

In addition, the sensing device 1 is not limited to a wearable sensor, but may be any device capable of measuring the movement of an individual or object, such as a device that measures the movement of an individual or object through image analysis, a laser radar that can detect the position of an object by reflecting laser light, a force plate that can measure pressure or impact, or other stationary sensors such as a sonic sensor, a magnetic sensor, an RGB camera, a depth sensor, or a multi-array microphone.

The sensing data regarding the movements measured by the sensing device 1 is transmitted to the server 5 via the network 4, where analysis processing etc. is carried out, and the analysis results are displayed on the display etc. of the input/output device 56.

The server 5 in this embodiment analyzes the sensing data received from the sensing device 1 to generate synchronized network time series data (described below) that indicates the cooperation or relationships between the individuals 60, and calculates the time series changes in the activity and diversity (structure) of the group as a group consciousness index from the synchronized network time series data. The server 5 visualizes the changes in the activity and structure of the group over time by outputting the group consciousness index as a plot diagram on a display or the like.

The server 5 is a computer having a CPU 51, a memory 54, an auxiliary memory device (storage device) 55, a communication unit 52, and an input/output device 56. In the following description, a network without a symbol is used to indicate collaboration between individuals, and is distinguished from the network 4 that connects computers.

The server 5 realizes the functions of the analysis unit 10 and the information input unit 53 by executing a program loaded from the auxiliary storage device 55 to the memory 54 with the CPU 51. The memory 54 of the server 5 can be configured with a ROM, which is a non-volatile storage element, or a RAM, which is a volatile storage element.

The ROM stores programs (e.g., BIOS) and the like. The RAM is a high-speed, volatile storage element such as DRAM (Dynamic Random Access Memory), and temporarily stores the programs stored in the auxiliary storage device 55 and data used when the programs are executed.

The database 20 is stored in the auxiliary storage device 55. The auxiliary storage device 55 is, for example, a large-capacity, non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD). The auxiliary storage device 55 also stores the programs executed by the CPU 51 and data used by the programs. That is, the programs are read from the auxiliary storage device 55, loaded into the memory 54, and executed by the CPU 51.

The server 5 has an information input unit 53 that stores the sensing data received from the sensing device 1 in the sensor data 22 of the database 20, and collects the sensing data transmitted at a predetermined interval.

The analysis unit 10 is a functional unit that calculates acceleration sensor and position information of each individual 60 in a time series based on the sensor data 22, calculates a network between individuals based on the movements and trajectories of the individuals detected from the acceleration sensor information and position information, and calculates a collective consciousness index W from the network. When cooperation or relationships between individuals in a competition or the like are represented by edges, a node that includes one or more edges is considered a clique. When one node is activated, it can also be treated as a clique. A clique can be treated as a complete partially connected network.

The analysis unit 10 mainly has the functional units of a feature calculation unit 11, a network analysis unit 12, a group consciousness analysis unit 13, and a display unit 14. Note that the location of an individual may be measured using location information detected by GNSS 35 or the like in addition to the acceleration of the sensing device 1 attached to the individual.

The display unit 14 provides the data recorded in the database 20 to a user (a user of the analysis system) who evaluates the group by outputting the data to a computer or device connected via the network 4 or to an input/output device 56. The input/output device 56 is composed of a keyboard, a mouse, a display, etc.

The CPU 51 operates as a functional unit that provides a specific function by processing according to the program of each functional unit. For example, the CPU 51 functions as the analysis unit 10 by processing according to an analysis program. The same applies to other programs. Furthermore, the CPU 51 also operates as a functional unit that provides each function of the multiple processes executed by each program. A computer and a computer system are devices and systems that include these functional units.

The communication unit 52 is a network interface that controls communication with other devices according to a specific protocol. The communication unit 52 can communicate with other devices via the network 4 and send and receive data.

The information input unit 53 functions as a sensing data collection unit that receives sensing data transmitted from the sensing device 1 and stores it in the sensor data 22 of the database 20. The information input unit 53 can also receive input from the input/output device 56 and reflect it in the database 20.

The analysis unit 10 and the information input unit 53 may be implemented by a program, or may be implemented by a physical integrated circuit. In particular, the analysis unit 10 may be implemented by multiple programs or multiple integrated circuits for executing the functional units included in the analysis unit 10. Furthermore, the feature calculation unit 11, the network analysis unit 12, the collective consciousness analysis unit 13, and the display unit 14 may each be implemented by multiple programs or multiple integrated circuits for each process that they execute.

The programs executed by the CPU 51 are provided to the server 5 via removable media (CD-ROM, flash memory, etc.) or a network, and are stored in the auxiliary storage device 55, which is a non-transitory storage medium. For this reason, the server 5 should have an interface for reading data from removable media.

The server 5 is a computer system that is physically configured on one computer, or logically or physically configured on multiple computers, and the above-mentioned programs may be executed in different threads on the same computer, or may be executed on virtual computers constructed on multiple physical computers.

The database 20 stores sensor data 22, feature information 21, synchronous network time series data 24, occurrence probability data 25, entropy data 30, and a collective consciousness graph 40.

The sensing data measured by the sensing device 1 of each individual 60 is collected by the information input unit 53 and stored as sensor data 22. In this embodiment, the sensor data 22 indicates the measurement results of the movement of each individual 60.

The feature information 21 indicates features related to the movement of each individual 60 calculated by executing the feature calculation unit 11 on the sensor data 22. Details will be described later.

The synchronous network time series data 24 indicates information about the network between individuals 60 calculated by the network analysis unit 12. Details will be described later.

The occurrence probability data 25 is data calculated in a time series of the probability that a clique that constitutes a network will appear. The entropy data 30 is time series data calculated using the occurrence probability data 25 to calculate the information entropy of a population, and will be described in detail later.

The collective consciousness graph 40 is composed of plot diagrams that plot the activity and structure of a group calculated based on information entropy in a time series. Plot diagrams for each number of cliques can be stored in the collective consciousness graph 40. The display unit 14 of the server 5 displays the plot diagrams read from the collective consciousness graph 40 on the display of the input/output device 56, etc., thereby functioning as a visualization unit that visualizes the consciousness of the group, consisting of changes in the activity and network structure of the group, in a time series.

The sensing device 1 is a device that measures the movement of a target individual for which a network is to be analyzed. In this embodiment, a wearable device worn by the target individual is used as an example. Note that the sensing device 1 may measure any content as sensing data, as long as the value changes as a result of the user's movement.

The sensing device 1 includes an acceleration sensor 31, a Global Navigation Satellite System (GNSS) 35, a memory 32, a microcomputer (MCU in the figure) 33, and a communication unit 34.

The acceleration sensor 31 measures the user's acceleration, for example, at a rate of about 20 to 1000 times per second. The GNSS 35 can be configured with a location information system such as the Global Positioning System (GPS) or the Quasi-Zenith Satellite System (QZSS), and measures the location information of the individual. In this embodiment, the location information from the GNSS 35 is used as the location information of the individual 60 at the start of the competition, and the sensing data of the acceleration sensor 31 is used to measure the movement of the individual 60 during the competition.

The microcomputer 33 records the measurement results obtained by the acceleration sensor 31 and the GNSS 35 as sensing data in the memory 32. Here, the microcomputer 33 records a unique identifier (user ID) for each user and the measurement results as sensing data.

The microcomputer 33 transmits the sensing data and location information recorded in the memory 32 to the server 5 via the communication unit 34. The communication unit 34 transmits the sensing data to the server 5, using a wireless or wired connection, at a timing when communication is possible or at a predetermined timing set by the user or administrator.

<Processing Overview>
FIG. 2 is a flowchart showing an example of a process performed by the analysis unit 10 of the server 5.

First, the feature calculation unit 11 acquires sensing data to be processed from the sensor data 22, and calculates feature information 21 related to movement for each individual 60 from the target sensor data 22 (S101).

The feature information 21 indicates a feature whose value changes according to the strength of the movement of the individual 60 (or the magnitude of the energy of the movement), such as the amount of body movement, and further indicates the change in the feature in a time series.

In this embodiment, the physical activity intensity is calculated as the characteristic amount, but any characteristic amount related to exercise, such as the amount of exercise, the amount of body movement, the moving speed, the frequency of activity, the pitch, the number of steps, etc., may be calculated as the characteristic amount. In addition, characteristic amounts that change with exercise, such as calorie consumption, heart rate, pulse rate, and amount of sweating, may also be calculated as the characteristic amount.

Next, the network analysis unit 12 uses the calculated feature information 21 to perform network analysis between the individuals 60 to generate synchronized network time series data 24 (S102). The group consciousness analysis unit 13 then executes steps S21 to S23 to extract cliques from the network between the individuals 60 generated, calculates the information entropy for each type of clique as described below, calculates the group consciousness index W (Index of group consciousness), and generates a plot diagram (S103).

The group consciousness analysis unit 13 first extracts cliques, which are activated nodes, from the synchronous network time series data 24 generated in step S102, calculates the occurrence probability for each type of clique, and generates the occurrence probability sum k _i for each time as occurrence probability data 25 (S21). As will be described later, the occurrence probability sum k _i is an index showing the activity level of the group. The subscript i of the occurrence probability sum k indicates the number of cliques.

Next, the group consciousness analysis unit 13 calculates the information entropy Hi at each time for each type of clique from the occurrence probability data 25. The group consciousness analysis unit 13 calculates the ratio of the information entropy Hi at each time to the information entropy Hi (max) of the network in a random state calculated for each type of clique, as an index showing the diversity (composition) of the group (S22).

The collective consciousness analysis unit 13 determines the set of the ratio of the total occurrence probability k _i for each time point calculated in step S21 and the information entropy Hi calculated in step S22 as a collective consciousness index W, and generates plot diagrams 400 shown in Figures 11 and 13 for each number of cliques and stores them in the collective consciousness graph 40 (S23).

The display unit 14 outputs the plot diagram stored in the group consciousness graph 40 to the display of the input/output device 56, providing the user of the network analysis system with a time series of the activity level of groups engaged in purposeful activities and changes in the network structure.

By executing the process shown in FIG. 2, the analysis unit 10 uses the sensor data 22 to analyze the network between the individuals 60 being analyzed, generates synchronous network time series data 24, and extracts cliques.

The group consciousness analysis unit 13 calculates a pair of the total occurrence probability k _i calculated for each type of clique and the ratio (or difference) between the information entropy Hi at each time and the information entropy Hi(max) of the network state in a random state as a group consciousness index W, and plots the group consciousness index W in space to generate a plot diagram. The plot diagram makes it possible to visualize the activity level of groups that are acting with a purpose and changes in the network structure in time series.

<Network analysis details>
3 to 6 show data used in the processes performed by the feature amount calculation unit 11 and the network analysis unit 12 and the contents of the processes. Fig. 3 is a diagram showing the sensor data 22 in the first embodiment.

The sensor data 22 includes a user information table 201 that stores information about the individual 60 wearing the sensing device 1, and a movement information table 202 that records activity level information for each individual 60.

The user information table 201 is pre-configured information, and includes a user ID 2011, a user name 2012, and a user type 2013 in one entry. The user ID 2011 stores a unique identifier that recognizes the individual 60 wearing the sensing device 1.

User name 2012 stores the name or nickname of the individual 60 wearing the sensing device 1. User type 2013 may record age, gender, etc. as user information, and also records various information of the individual 60 that is not limited to these.

The movement information table 202 stores the sensing data for each time received from the sensing device 1. The movement information table 202 includes a user ID 2021, a measurement date and time 2022, an acceleration X-axis 2023, an acceleration Y-axis 2024, an acceleration Z-axis 2025, a latitude 2026, and a longitude 2027 in one entry.

User ID 2021 stores a user ID for identifying individual 60, and corresponds to user ID 2011. Measurement date and time 2022 stores the date and time when sensing data was measured. Acceleration X-axis 2023, acceleration Y-axis 2024, and acceleration Z-axis 2025 store the measurement results of each axis of the three-axis acceleration sensor. Latitude 2026 and longitude 2027 store the measurement values of GNSS 35. Note that the movement information table 202 can store a body movement frequency (not shown) calculated from the acceleration data. The body movement frequency can be calculated by server 5, but may also be calculated by sensing device 1.

Figure 4 is a diagram showing an example of data stored in the feature information 21. The feature calculation unit 11 calculates METs (Metabolic Equivalents) indicating exercise intensity as feature amounts for the sensor data 22 of all individuals 60 constituting the group. The calculation of feature amounts is similar to that of Patent Document 1.

The feature information 21 stores the features at each date and time 222 of all individuals 223 for which network analysis was performed. In this embodiment, METs are calculated as features from sensor data 22 measured at a predetermined sampling frequency of 20 Hz, and the values tallied every second are stored.

Figure 5 is a flowchart showing the details of step S102 in Figure 2 performed by the network analysis unit 12 in Example 1. The network analysis unit 12 analyzes the relationships between individuals that make up the population from the features calculated in step S101.

First, in step S1021, the network analysis unit 12 acquires the time interval of the data used to analyze the network between the individuals 60. The time interval of the data may be a preset value as long as it is equal to or greater than the measurement interval (sampling period or a predetermined time interval) of the sensing device 1.

In step S1022, the network analysis unit 12 selects two individuals for network analysis. In this embodiment, two individuals are selected in sequence to analyze the presence or absence of correlation between the two individuals for all combinations of individuals that make up the population.

In step S1023, for the individual 60 selected in step S1022 above, the features calculated in step S101 of FIG. 2 are obtained from the feature information 21.

In steps S1024, S1025, and S1026, the relationship between the two individuals is analyzed to determine whether or not a network exists. In this embodiment, the amount of information transmitted between the individuals 60 is calculated using transfer entropy to determine whether or not a network exists.

Transfer entropy is a type of information amount, and is a numerical representation of the flow of information between random variables as the average amount of information. Unlike mutual information, which indicates only the correlation between variables and does not include the flow of information, transfer entropy can identify the flow of information and is an index that measures the relationship between variables. In this embodiment, transfer entropy is used, but any index that measures the relationship between multiple variables, such as mutual information, correlation analysis, regression analysis, multivariate analysis, etc., and any method for calculating said index may be used.

In step S1024, the network analysis unit 12 calculates a probability distribution from the time series data of the features of each individual 60. A method of approximating the probability distribution using a histogram is used, but a method of estimating by assuming a Gaussian distribution or a mixed Gaussian distribution may also be used, and the method is not limited to these. When approximating using a histogram, the number of divisions into the histogram is determined using the Akaike Information Criterion or Sturges's formula, but is not limited to these.

In step S1025, the network analysis unit 12 calculates the amount of information transmission between the variables of the two individuals 60 from the transfer entropy. The transfer entropy is calculated in the same manner as in Patent Document 1.

In step S1026, the network analysis unit 12 uses the value of transfer entropy calculated in step S1025 to determine whether or not a network exists between two individuals. The network analysis unit 12 determines that information is transmitted between individuals 60 whose transfer entropy is equal to or greater than a predetermined threshold, and that a network exists. In this embodiment 1, the threshold is a predetermined constant. The threshold may be a threshold determination method using a x2 distribution, a gamma distribution, or the like, and is a value that is preset depending on the sensing device 1 used, the environment in which the measurement is performed, and the like.

In step S1027, the network analysis unit 12 stores the presence or absence of the network determined in step S1026 above in the synchronous network time series data 24.

In this embodiment, if a network exists between two individuals, a value corresponding to the transfer entropy is set in the synchronization network time series data 24, and if no network exists, the synchronization network time series data 24 is set to 1. The value corresponding to the transfer entropy is determined by setting multiple thresholds and judging the network strength in several stages.

In step S1028, steps S1022 to S1027 are repeated until analysis of all combinations of individuals that make up the population is completed.

By the above process, the magnitude of the relationship between the individuals 60 is calculated based on the magnitude of the transfer entropy, the individuals that make up the network are identified, and a network within the population is generated.

Figure 6 is a diagram showing data stored in the synchronous network time series data 24 of Example 1. The synchronous network time series data 24 is a brute force diagram of the network at a certain time, and describes the presence or absence (strength or weakness) of the network at a certain time t for all individuals for which network analysis was performed.

The synchronous network time series data 24 is time series data generated at a predetermined time interval. In the illustrated example, the synchronous network time series data 24 is generated at a predetermined time interval ΔT from time T1.

The first column of the synchronous network time series data 24 is the information and the user ID 242 of the influencing individual, and the first row is the information and the user ID 243 of the affected individual. The value between two individuals shown by the row and column indicates the strength of the network on a five-point scale.

When the relationship between the individuals 60 is strong, "5" is set, and when the relationship between the individuals 60 is weak, "1" is set. Note that in this embodiment, the strength of the relationship is described in five stages, but it may be expressed in any number of stages. Note that the strength of the relationship between the individuals 60 can be expressed by degree centrality or closeness centrality, as disclosed in the above-mentioned Patent Document 1.

Furthermore, the synchronous network time series data 24 is not limited to the strength of the relationship between the individuals 60, but can be expressed by the presence or absence of the relationship. For example, if there is a relationship between the individuals 60, a "1" may be set, and if there is no relationship between the individuals 60, a "0" may be set.

Figure 7 is a diagram showing the relationship between the movement information table 202 of the sensor data 22 and the synchronous network time series data 24. The illustrated example shows a portion of the synchronous network time series data 24 generated from the acceleration data obtained from the movement information table 202 as a graph, and shows the time-series changes in the relationships between multiple individuals 60.

At time t1, the individual 60 with ID="1" has relationships with two individuals 60 with ID="2" and "4", and the individual 60 with ID="3" has a relationship with an individual 60 with ID="6". At time t2, when a predetermined time interval Δt (for example, one minute) has elapsed, ID="1" changes the individual 60 with which it is related from ID="4" to ID="5", and has relationships with two individuals 60 with ID="2" and "5". At time t3, ID="1" has only one individual 60 with ID="5".

The synchronous network time series data 24 is a network of synchronization between individuals 60, and by comparing the synchronous networks between individuals 60 over time, it is possible to analyze sports involving intense group movements, such as soccer, rugby, or basketball.

Figure 8 shows an example of extracting a clique from the synchronous network time series data 24. As shown in Figure 7, the network analysis unit 12 can generate a graph from the synchronous network time series data 24 and extract changes in the related destinations of an individual 60 in time series.

The network analysis unit 12 extracts a predetermined number of cliques from the synchronous network time series data 24, and aggregates and outputs the cliques at each time in time series. In this embodiment, as shown in the figure, 1 clique consisting of one individual 60, 2 cliques consisting of two individuals 60, and 3 cliques consisting of three individuals 60 are extracted as evaluation targets.

The graph at time t1 in FIG. 7 can be decomposed into the relationships of 1 clique, 2 cliques, and 3 cliques shown in FIG. 8. In FIG. 8, the network analysis unit 12 extracts activated nodes from among the nodes constituting the graph as 1 clique. Activated nodes can be, for example, nodes whose body movement frequency obtained from the movement information table 202 is equal to or greater than a predetermined threshold (e.g., 2 Hz) that can be extracted by the network analysis unit 12 as 1 clique.

Next, the network analysis unit 12 connects related individuals 60 with one edge, and extracts a graph with the nodes at both ends of the edge as 2 cliques. In other words, a combination of nodes with degree 1 forms a 2 clique. For example, in the graph at time t1, IDs "1" and "2" form 2 cliques, and furthermore, IDs "1" and "4", IDs "2" and "4", and IDs "3" and "6" are extracted as 2 cliques. Note that in this embodiment, the direction of the edges is not evaluated, and the graph is treated as an undirected graph.

When the destination node of a two-clique relationship is related to a third node, and the source node of the relationship is also related to the same third node, the network analysis unit 12 extracts these three nodes as a three-clique.

Each node in the 3-clique has degree 2, and the three nodes are related to each other. In the graph at time t1, in the 2-clique with IDs "1" and "2", node "1" is the source of the relationship, and node "2" is the destination of the relationship of "1". The destination node "2" has a relationship with another (third) node "4", and node "1" of the source of the relationship has a relationship with the same other node "4". In this way, a configuration in which the edges connecting the three nodes are closed is called a 3-clique.

<Definition of collective consciousness>
In a group of unrelated strangers who happen to be in the same place, if a network (synchronous network or communication network) is generated by the synchronization of body movements, it will be a network with uniform random connections. This is because even if it appears synchronized, it is presumed to be by chance.

On the other hand, if a group is acting with some kind of purpose, it is believed that the network structure will be different from that of "a group of unrelated strangers who happen to be in the same place." Therefore, in this embodiment, the extent to which the network structure of a "group acting with a purpose" differs from a network with uniform random connections is quantified and used as an index representing "group consciousness."

In this embodiment, a collective consciousness index W that represents the "consciousness of a group" is defined using cliques (= attributes) that arise in a synchronous network, and the difference from a uniformly random group is visualized, making it possible to distinguish between the two.

When one individual 60 is one node and the nodes in the population are activated randomly and with equal probability, the sum _k1 of the probabilities _pj that one node is activated is given by:

where 0≦k ₁ ≦n. If the information entropy of a state in which one node is activated with equal probability in a random network is H ₁ (max), then this can be expressed by the following equation (2).

In the case of a network structure consisting of n nodes, the information entropy in the state in which one node is activated can be expressed by the following equation (3), where _H1 is the information entropy of the state in which one node is activated.

The difference _I1 in information entropy between a random network and the activation of n nodes is given by

I ₁ = H ₁ (max) - H ₁

The difference in information entropy _I1 indicates how far away each node is from the case where each node is randomly activated with equal probability, in other words, it indicates how well the group of information entropy _H1 is networked.

The difference _I1 is zero when "all nodes are activated with probability 1", "no nodes are activated with probability 1", and "all nodes have equal activation probability". The difference _I1 is maximum when the activated node is fixed. When the node is fixed, for example, it is a state in which the relationship between nodes does not change, indicating a state of low diversity. The above is an example of a network structure in which one clique appears when one node is activated.

Next, consider a two-clique network structure in which two nodes are activated and have a relationship. The sum _k2 of the probability _pjq that two nodes are activated and connected is expressed by the following equation (4).

where j and q indicate the identifiers of the connected nodes. The result of calculating the probability that a node will be activated is stored in the occurrence probability data 25 shown in FIG. 9.

The occurrence probability data 25 is calculated using a plurality of time series networks. The number of time series networks that calculate the occurrence probability data 25 is adjusted as a parameter according to the measurement target. In addition, the time interval for updating the occurrence probability data 25 is also adjusted as a parameter according to the measurement target. Therefore, the occurrence probability data 25 is generated at a certain time interval determined to be optimal according to the measurement target, and components 251 and probabilities 252 are set for each of 1 clique, 2 cliques, and 3 cliques. For example, when a group is composed of 6 nodes as shown in the figure, the occurrence probability p _j of 1 clique is 6, from p ₁ to p _6. In addition, the occurrence probability p _jq of 2 cliques is 15, from p ₁₂ to p _56. In addition, the occurrence probability p _jqr of 3 cliques is 20, from p ₁₂₃ to p _456. In addition, j, q, and r indicate the identifiers of the connected nodes.

The information entropy of a state (2-clique) in which two activated nodes j and q are randomly connected with equal probability is maximum value H ₂ (max) and is expressed by the following formula (5).

Here, N indicates the number of combinations of connected nodes. When there is a network structure consisting of n nodes, the information entropy of the state in which two nodes are connected can be expressed by the following formula (6), where _H2 is the information entropy of the state in which two nodes are connected.

The difference _I2 between the information entropy H2(max) of a network in which two nodes j and q are randomly connected with equal probability and the information entropy I2 when the two nodes are connected is given by

I ₂ = H ₂ (max) - H ₂

The difference in information entropy _I2 indicates how far the two cliques in a population are from a state where they are randomly connected with equal probability, that is, how organized they are.

The difference _I2 in information entropy becomes zero when "all nodes are connected with probability 1", "no nodes are connected with probability 1", and "all paths have the same probability of appearing". The difference _I2 becomes maximum when the network structure is fixed.

Next, consider a 3-clique network structure in which three nodes are activated and have a relationship. The sum _k3 of the probabilities _pjqr that three nodes are activated and connected is expressed by the following equation (7).

Here, j, q, and r indicate the identifiers of the connected nodes. The information entropy H ₃ (max) of a state in which three activated nodes j, q, and r are randomly connected with equal probability (3-clique) is expressed by the following equation (8).

In the case of a network structure consisting of n nodes, the information entropy in the state in which three nodes j, q, and r are connected can be expressed by the following equation (9), where _H3 is the information entropy.

The difference _I3 between the information entropy _H3 (max) of a network in which three nodes j, q, and r are randomly connected with equal probability and the information entropy _H3 when two nodes are connected is given by

I ₃ = H ₃ (max) - H ₃

The difference in information entropy _I3 represents how far the 3 cliques in the population are from a state where they are randomly connected with equal probability, that is, how organized they are.

As with the above 2-clique case, the difference _I3 is zero when "all 3-cliques appear with probability 1", "no 3-cliques appear with probability 1", and "all 3-cliques have equal appearance probability". Also, the difference _I3 is maximum when the network structure is fixed.

As described above, the difference in entropy based on the network in a random state can be used as an index of collective consciousness. In this embodiment, the collective consciousness index W is defined by the following formula (10) using the ratio of the information entropy H _i (max) in the random state to the information entropy H _i , where i is the number of cliques.

For example, in sports, the network within a team tries to approach perfect connection, but cannot be realized because of the presence of an opposing team. The opponent also tries to approach perfect connection, so there is always pressure to be completely unconnected. Since the network structure is not fixed but changed to respond, the element of the entropy ratio of the group consciousness index W transitions between states that are neither 0 nor 1. Such a state is defined as the group being conscious. In addition, the larger the value of the total probability of occurrence k _i of the group consciousness index W, the higher the activity of the group.

Note that in the above example, i = 1 to 3 indicates the number of cliques, but the number of cliques i may be 4 or more.

<Collective Consciousness Analysis Processing>
10 is a flow chart showing an example of processing carried out by the collective consciousness analysis unit 13. The collective consciousness analysis unit 13 acquires the synchronous network time series data 24 generated by the network analysis unit 12 (S211).

The collective consciousness analyzer 13 extracts a prescribed number (i) of cliques from the synchronous network time series data 24, calculates the occurrence probability _pj of each clique, and generates occurrence probability data 25 for each specified time interval (S212).

The collective consciousness analyzer 13 calculates the sum of occurrence probabilities _pj for each predetermined number of cliques from the occurrence probability data 25, and calculates the occurrence probability sum _ki for each specified time interval (S213). If the number of cliques is 1, the occurrence probability sum _k1 is calculated using the above formula (1), if the number of cliques is 2, the occurrence probability sum _k2 is calculated using the above formula (4), and if the number of cliques is 3, the occurrence probability sum _k3 is calculated using the above formula (7).

Next, the collective consciousness analyzer 13 calculates the information entropy H _i for each specified time interval from the total occurrence probability k _i for each number of cliques i, and stores this in the entropy data 30 (S221). The entropy data 30 stores the information entropy H _i of the state for each time interval specified for each type (number) of cliques i.

When the number of cliques is 1, the information entropy _H1 is calculated using the above formula (3). When the number of cliques is 2, the information entropy _H2 is calculated using the above formula (6). When the number of cliques is 3, the information entropy _H3 is calculated using the above formula (9).

Furthermore, the collective consciousness analysis unit 13 calculates the information entropy H _i (max) of the network in a random state for each number of cliques (i). When the number of cliques is 1, the information entropy H ₁ (max) of the random state is calculated using the above formula (2), when the number of cliques is 2, the information entropy H ₂ (max) of the random state is calculated using the above formula (5), and when the number of cliques is 3, the information entropy H ₃ (max) of the random state is calculated using the above formula (8).

The collective consciousness analysis unit 13 calculates an index of diversity (hereinafter, diversity index) for each specified time interval by subtracting from 1 the ratio H _i /H _i (max) of the information entropy H _i (max) of the network in a random state and the information entropy H _i for each specified time interval (1-H _i /H _i (max)) (S222).

The collective consciousness analysis unit 13 plots a pair of the total occurrence probability k _i for each specified time interval and the diversity index for each clique type i as a collective consciousness index W in a two-dimensional space to generate a plot diagram and stores it in the collective consciousness graph 40 (S23). The generated plot diagram is displayed on the display of the input/output device 56 by the display unit 14. The server 5 may transmit the plot diagram to another computer connected via the network 4 to display it.

Through the above processing, an index of diversity is calculated for each specified time interval based on the ratio of the information entropy H _i of the state of each type i of clique calculated from the synchronous network time series data 24 to the information entropy H _i (max) of the network in a random state, and the sum k _i of the occurrence probability of cliques for each specified time interval is calculated as activity, and a pair of the diversity index and activity for each specified time interval is generated as a collective consciousness index W.

Then, the group consciousness analysis unit 13 plots pairs of activity and diversity index for each specified time interval in two-dimensional space to generate a plot diagram showing the consciousness of the group.

When generating plot diagrams for multiple clique numbers, the processes in steps S211 to S23 above may be performed in parallel, or may be performed sequentially for each clique number.

<Plot diagram>
11 is a plot diagram 400 showing the consciousness of a group. The plot diagram 400 is a graph in which the diversity index (1-H _i /H _i (max)) is plotted on the vertical axis and the activity level (sum of occurrence probability k _i ) is plotted on the horizontal axis in the time series of the synchronous network time series data 24.

As for the characteristics along the vertical axis, the closer the diversity index is to 1, the more fixed the cliques that appear. On the other hand, as the diversity index is closer to 0, the probability of cliques appearing becomes equal. As for the characteristics along the horizontal axis, the closer the activity level (total occurrence probability k _i ) is to 0, the lower the group activity (communication), and as the activity level increases, the group activity becomes more active. In this embodiment, synchronization of body movements is treated as unconscious communication.

The characteristics of each region of the plot 400 are as follows. In the region 401 where the diversity index is close to 1 and the activity level is close to 0, certain individuals 60 are active or all of the individuals 60 are inactive, activity (communication) within the group is low, and the diversity of relationships between the individuals 60 is low.

In the region 402 where the diversity index is close to 1 and the activity level is high, all the individuals 60 are active and activity (communication) within the group is vigorous, but the diversity of the relationships between the individuals 60 is low.

In the region 403 where the diversity index is close to 0 and the activity level is also close to 0, certain individuals 60 are active or all individuals 60 are inactive, and activity (communication) within the group is low, but the diversity of relationships between the individuals 60 is high.

In the region 404 where the diversity index is close to 0 and the activity level is high, all the individuals 60 are active, activity (communication) within the group is vigorous, and the relationships between the individuals 60 are highly diverse.

Figure 12 is a plot diagram 400 showing the consciousness of a group. Based on the characteristics of areas 401-404 shown in Figure 11, when plot diagram 400 is created for a conscious group, values are often plotted in area 410 shown.

In Figure 12, this is an area that spans areas 401 and 404, and the plotted position changes over time between a state in which all individuals 60 are inactive and have low diversity (area 401) and a state in which all individuals 60 are active and have high diversity (area 404).

Figure 13 is a plot 400 of a population analysis of an athletic field. The example shown is a three-clique plot 400 showing the population consciousness of two athletic teams of individuals 60 over time.

In team sports, it is said that "when one team is pushed back by the opposing team, organizational movements become monotonous." In the example shown, the values of the underdog team B are concentrated in the upper left area of the figure, i.e., area 401 in Figure 11, visualizing that all members are inactive and communication (diversity) within the team is low. When a team is being pushed back by the opposing team, it is thought that the network structure has no choice but to become monotonous in order to maintain information transmission within the team.

Note that, while an example of a plot diagram 400 with three cliques was shown above, a similar trend can be seen in a plot diagram with two cliques, making it possible to visualize the consciousness of a group over time.

As described above, in this embodiment, by calculating the total occurrence probability k _i and the information entropy H _i for each completely connected partial network (=clique) at specified time intervals and pairing them with the collective consciousness index W, it becomes possible to quantify how far the group being analyzed is from a random network.

Then, the group consciousness analysis unit 13 plots the group consciousness index W for each clique type i on a two-dimensional plane, making it possible to represent the activity level (= group temperature) on the horizontal axis of the graph and the complexity of the network structure (= diversity) on the vertical axis.

Figure 14 shows Example 2, which is a diagram showing an example of a network analysis system applied to the field of education. Example 1 shows an example in which the group consciousness index W is applied to unconscious communication such as synchrony of body movements. Example 2 shows an example in which the group consciousness index W is applied to conscious communication such as face-to-face communication and conversation.

In Example 2, a sensing device 1A, which is the sensing device 1 of Example 1 with an infrared communication unit 36 added thereto, is attached to an individual 60, and face-to-face meetings (meetings) between individuals 60 are detected and transmitted to the server 5 for storage in the sensor data 22.

In the server 5, the network analysis unit 12 generates a meeting matrix 23 of the individuals 60 who have met from the sensor data 22 for each specified time interval, and generates communication network time series data 24A from the meeting matrix 23.

The server 5 calculates the group consciousness index W consisting of a pair of a diversity index and activity level for each number of cliques from the communication network time series data 24A in the same manner as in Example 1, and generates a plot diagram.

In this embodiment, the network analysis unit 12 generates communication network time series data 24A from a meeting matrix 23 instead of the synchronization network time series data 24 of the first embodiment, but the other configurations are the same as those of the first embodiment.

In addition to the contents of Example 1, the sensor data 22 includes the ID of the person (individual 60) detected by the infrared communication unit 36. Calculation of the body movement frequency is the same as in Example 1.

Figures 15 and 16 show an example of the relationship between the meeting matrix 23 and the communication network time series data 24A at times T1 and T2.

In the meeting matrix 23 in Figures 15 and 16, ID231 and ID232 indicate the identifiers (user IDs) of the individuals 60 that make up the group, and the body movement frequency of each individual 60 is stored. The network analysis unit 12 can determine that an individual 60 whose body movement frequency is equal to or greater than a predetermined threshold value (e.g., 2 Hz) is in a speaking state (activated).

At time T1 in FIG. 15, individual 60 with user ID=1 is in a speaking state, conversing with users ID=2 and 4 in a face-to-face state, and user ID=4 is speaking to user ID=2.

At time T2 in FIG. 16, individual 60 with user ID=1 is in a speaking state, conversing with users ID=2 and 5 in a face-to-face state, and user ID=5 is speaking to user ID=4.

The network analysis unit 12 generates communication network time series data 24A for each specified time interval from the above-mentioned meeting matrix 23. Note that the generation of the communication network time series data 24A is not limited to the above, and any publicly known or well-known technology may be applied. For example, as disclosed in the above-mentioned Patent Document 2, the communication network time series data 24A can be created using a spring model, with each individual 60 as a node and the meeting time as the attractive force between the nodes.

Figure 17 is a plot 400 of a group analysis in the field of educational activities. The example shown is a plot 400 of one clique that shows the collective consciousness of two groups of individuals 60 engaged in group learning over time.

The illustrated example is a plot diagram showing the results of group learning by two groups, Group A and Group B. Group A is distributed from the center to the right side of the plot diagram 400, and is always able to engage in conscious group activities.

In contrast, Group B is distributed temporarily to the right side of plot 400, but the majority of the group is distributed in the lower left part of plot 400, making it visible that the conscious group activity is temporary.

As described above, according to Example 2, by applying the group consciousness index W to conscious communication such as face-to-face meetings and conversations, it becomes possible to quantify and visualize the consciousness of a group, which can be reflected in group management, etc.

<Conclusion>
As described above, the network analysis systems of the first and second embodiments can be configured as follows.

(1) A network analysis method in which a computer (server 5) including a processor (CPU 51) and a memory (54) analyzes a network between individuals (60) in a population, the network analysis method comprising the steps of: a sensing data collection step (information input unit 53) in which the computer (5) collects time-series information on the activities of the individuals (60) constituting the population and stores the information in sensor data (22); a network analysis step (network analysis unit 12) in which the computer (5) extracts activated individuals (60) from the sensor data (22) as cliques and generates a network (synchronous network time-series data 24) composed of the cliques in time series; and a group consciousness analysis step (group consciousness analysis unit 13) in which the computer (5) calculates a sum k _i of the occurrence probability of the cliques and an information entropy H _i of the cliques, and calculates an index indicating the activity level of the population and the diversity of the population as a group consciousness index (W).

With the above configuration, by quantifying the index showing the activity level and diversity of a group as the group consciousness index W, it becomes possible to visualize the time-series changes in the network structure of a group that is active with a purpose, and it becomes possible to easily grasp the consciousness of the group.

(2) The network analysis method according to (1) above, wherein the group consciousness analysis step (13) includes the steps of: calculating a sum (k _i ) of the occurrence probabilities (pj) of the cliques for each specified time interval as an activity level of the individual; calculating an information entropy of the network in which the individual (60) is in a random state as a first information entropy (H _i (max)); calculating an information entropy of the network of the individual (60) for each specified time as a second information entropy (H _i ); calculating a value based on a ratio between the first information entropy (H _i (max)) and the second information entropy (H _i ) as a diversity index for each time; and calculating a pair of the activity level and the diversity index as a group consciousness index (W) for each specified time interval.

The above configuration makes it possible to calculate the collective consciousness index W, which is a value that quantifies the extent to which the network structure of a group acting with a purpose differs from a network with uniform random connections.

(3) The network analysis method described in (2) above, wherein the computer (5) further includes a visualization step (display unit 14) for visualizing the collective consciousness index (W), the collective consciousness analysis step (13) further includes a step of generating a plot diagram (400) in which the collective consciousness index (W) for each specified time interval is plotted in a predetermined space, and the visualization step (14) outputs the plot diagram (400).

The above configuration makes it possible to easily grasp how consciousness, which is made up of the activity level and diversity of a group, has changed by plotting pairs of activity level and diversity index.

(4) The network analysis method according to (1) above, wherein the group consciousness analysis step (13) calculates the group consciousness index (W) for each number (i) of cliques.

With the above configuration, the group consciousness index W can be easily understood by selecting the number of cliques (i) in the plot diagram 400 according to the characteristics of the group being analyzed. For example, in group sports such as soccer or rugby, the group consciousness index W can be easily understood by a plot diagram 400 with two or three cliques. Also, in the fields of education and business, the group consciousness index W can be easily understood by a plot diagram 400 with one or two cliques.

(5) The network analysis method according to (2) above, wherein the group consciousness analysis step (13) calculates 1-second information entropy (H _i )/first information entropy (H _i (max)) for each time interval designated as the diversity index.

With the above configuration, it is possible to calculate an index showing the diversity (structure) of a group from the information entropy (H _i /H _i (max)) of a network with uniform random connections and the information entropy H _i of the network portion of a group acting with a specific purpose.

(6) The network analysis method described in (1) above, wherein the sensing data collection step (53) collects sensor data (22) including acceleration data of the individuals (60) as time-series information on the activity of the individuals (60), and the network analysis step (12) calculates the relationships between the individuals (60) from the acceleration data to generate a network (24) composed of the cliques in time series.

With the above configuration, when analyzing a group in a sports field or the like, it is possible to generate synchronous network time series data 24 based on acceleration data, and calculate a group consciousness index W according to the group's activities.

(7) The network analysis method described in (1) above, wherein the sensing data collection step (53) collects sensor data (22) including face-to-face information between the individuals (60) as time-series information on the activities of the individuals (60), and the network analysis step (12) calculates the relationships between the individuals (face-to-face matrix 23) from the face-to-face information to generate a network (communication network time-series data 24A) composed of the cliques in time series.

With the above configuration, when analyzing groups in fields such as education and business, communication network time series data 24A can be generated based on face-to-face information, making it possible to calculate a group consciousness index W according to the group's activities.

The present invention is not limited to the above-described embodiments, but includes various modified examples. For example, the above-described embodiments are described in detail to clearly explain the present invention, and are not necessarily limited to including all of the configurations described. It is also possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, the addition, deletion, or replacement of part of the configuration of each embodiment with other configurations can be applied alone or in combination.

The above configurations, functions, processing units, and processing means may be realized in part or in whole in hardware, for example by designing them as integrated circuits. The above configurations and functions may be realized in software by a processor interpreting and executing a program that realizes each function. Information on the programs, tables, files, etc. that realize each function may be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

In addition, the control lines and information lines shown are those considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be assumed that almost all components are interconnected.

REFERENCE SIGNS LIST 1 Sensing device 5 Server 10 Analysis unit 11 Feature amount calculation unit 12 Network analysis unit 13 Collective consciousness analysis unit 14 Display unit 21 Feature amount information 22 Sensor data 24 Synchronous network time series data 25 Occurrence probability data 30 Entropy data 40 Collective consciousness graph 51 CPU
52 Memory 55 Auxiliary storage device

Claims (15)

A network analysis method in which a computer including a processor and a memory analyzes a network between individuals in a population, comprising:
a sensing data collection step in which the computer collects time-series information on the activities of individuals constituting the population and stores the information in sensor data;
a network analysis step in which the computer extracts the individuals activated from the sensor data as cliques and generates a network composed of the cliques in a time series;
and a group consciousness analysis step in which the computer calculates the occurrence probability of the clique and the information entropy of the clique, and calculates an index indicating the activity of the group and the diversity of the group as a group consciousness index. 2. The network analysis method of claim 1, comprising:
The collective consciousness analysis step includes:
calculating the sum of the occurrence probabilities of the cliques for each specified time interval as the activity of the individual;
Calculating an information entropy of a network in a random state of the individuals as a first information entropy;
calculating an information entropy of the network of individuals for each of the specified time intervals as a second information entropy;
calculating a value based on a ratio between the first information entropy and the second information entropy as a diversity index for each designated time interval;
calculating a pair of the activity level and the diversity index as a group consciousness index for each specified time interval;
A network analysis method comprising: 3. A network analysis method according to claim 2, comprising:
The method further includes a visualization step of the computer visualizing the collective consciousness index,
The collective consciousness analysis step includes:
generating a plot of the collective consciousness index for each of the specified time intervals in a predetermined space;
The visualization step includes:
A network analysis method comprising the steps of: outputting the plot diagram. 2. The network analysis method of claim 1, comprising:
The collective consciousness analysis step includes:
A network analysis method, comprising: calculating the collective consciousness index for each number of cliques. 3. A network analysis method according to claim 2, comprising:
The collective consciousness analysis step includes:
A network analysis method comprising the steps of: calculating 1-second information entropy/first information entropy as the diversity index for each time interval designated. 2. The network analysis method of claim 1, comprising:
The sensing data collection step includes:
collecting sensor data including acceleration data of the individual as time-series information regarding the activity of the individual;
The network analysis step includes:
A network analysis method comprising the steps of: calculating relationships between individuals from the acceleration data; and generating a network composed of the cliques in time series. 2. The network analysis method of claim 1, comprising:
The sensing data collection step includes:
Collecting sensor data including face-to-face information between the individuals as time-series information regarding the activities of the individuals;
The network analysis step includes:
A network analysis method comprising the steps of: calculating relationships between individuals from the face-to-face information; and generating a network composed of the cliques in time series. a server including a processor and a memory;
A network analysis system including:
The sensing device transmits time-series information regarding individuals performing activities in a group to the server;
The server,
a sensing data collection unit that collects time-series information on the individual received from the sensing device and stores the information in sensor data;
a network analysis unit that extracts the activated individuals from the sensor data as cliques and generates a network composed of the cliques in a time series;
a group consciousness analysis unit that calculates the occurrence probability of the clique and the information entropy of the clique, and calculates an index indicating the activity level of the group and the diversity of the group as a group consciousness index;
A network analysis system comprising: 9. The network analysis system of claim 8,
The collective consciousness analysis unit:
Calculating the sum of the occurrence probabilities of the cliques for each specified time interval as the activity of the individual;
Calculating an information entropy of a network in a state in which the individuals are in a random state as a first information entropy;
Calculating an information entropy of the network of the individuals for each of the specified time intervals as a second information entropy;
calculating a value based on a ratio between the first information entropy and the second information entropy as a diversity index for each designated time interval;
A network analysis system characterized in that a pair of the activity level and the diversity index is calculated as a group consciousness index for each specified time interval. 10. The network analysis system of claim 9,
A visualization unit that visualizes the collective consciousness index,
The collective consciousness analysis unit:
generating a plot of the collective consciousness index for each of the specified time intervals in a predetermined space;
The visualization unit is
A network analysis system that outputs the plot diagram. 9. The network analysis system of claim 8,
The collective consciousness analysis unit:
A network analysis system, comprising: a network analysis unit that calculates the collective consciousness index for each number of cliques; 10. The network analysis system of claim 9,
The collective consciousness analysis unit:
A network analysis system comprising: a network analysis unit that calculates 1-second information entropy/first information entropy as the diversity index for each time interval designated. 9. The network analysis system of claim 8,
The sensing data collection unit includes:
collecting sensor data including acceleration data of the individual as time-series information regarding the activity of the individual;
The network analysis unit is
A network analysis system characterized by calculating relationships between individuals from the acceleration data and generating a network composed of the cliques in time series. 9. The network analysis system of claim 8,
The sensing data collection unit includes:
Collecting sensor data including face-to-face information between the individuals as time-series information regarding the activities of the individuals;
The network analysis unit is
A network analysis system characterized by calculating relationships between individuals from the face-to-face information and generating a network composed of the cliques in time series. A server including a processor and a memory,
a sensing data collection unit that collects time-series information on individuals performing activities in a group and stores the information in sensor data;
a network analysis unit that extracts the activated individuals from the sensor data as cliques and generates a network composed of the cliques in a time series;
a group consciousness analysis unit that calculates the occurrence probability of the clique and the information entropy of the clique, and calculates an index indicating the activity level of the group and the diversity of the group as a group consciousness index;
A server comprising:

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