Overview of related concepts of social network analysis

One 、

Network density = Total number of current relationships / Theoretical maximum number of relationships , The higher the overall network density , The greater the impact on the individual

Reciprocity It refers to whether the relationship between members in the network has reciprocity , In other words, whether any pair of members interact with each other “ choice ”, Whether it is an adjacency point .

Two 、

Centrality -> individual , Central potential -> Groups

Central potential ( centralization) Depict the difference degree of each point of the whole network , So a network has only one central potential .

Degree center potential ： The idea of calculating the central potential is also intuitive ： Find the most central point in the diagram , Calculate the difference between the centrality of this point and that of other points . That is to quantitatively discuss the imbalance of the centrality distribution of each point in the graph . The greater the difference , The more uneven the distribution of the centrality of each point in the graph , The larger the central potential of the graph —— The network is likely to be spread around the core point . 

The same is done for normalization , The central potential of the graph is defined as the sum of the actual differences / The sum of the maximum differences . therefore , The central potential of a complete graph is 0（ Every point is related to each other , It doesn't matter if the center is not the center ）, The central potential of a star shaped or radial network is close to 1.

Intermediate central potential ： It is also an index to analyze the overall structure of the network , Its meaning is the gap between the intermediate centrality of the node with the highest intermediate centrality in the network and that of other nodes . The greater the gap between this node and other nodes , The higher the central potential of the network , It means that the nodes in the network may be divided into several small groups and rely too much on one node to transfer relations , This node plays an extremely important role in the network .

Near center potential ： For a social network , The higher the potential near the center , It shows that the greater the difference of nodes in the network , conversely , It indicates that the difference between nodes in the network is smaller .

Point degree centrality 【 Degree centrality 】 It is an index used to measure the position of nodes in the network , The idea of point degree centrality is : If a point is connected to many nodes , Then the node is in the network Just It's about On Than a in heart Of position Set up , have Yes Than a Big Of “ right ”. It is a common method to measure the centrality of a point by the number of points directly connected to the node .

Close to centrality analysis “ distance ” Is the length of the shortest path between two points , The concept of near centrality is used to measure the centrality of a point . In a picture , The smaller the sum of the distances from one point to all other points , Show that this point is not affected by others “ control ” The more capable , The closer to the center . Such points have the best view in the network , You can know what happens in the network , And the direction of information flow .

Intermediate centrality 【 Intermediary centrality 】 In the network , If an actor is on the path between many other two points , It can be considered that the actor is in an important position , Because he has the ability to control the interaction between the other two actors .

3、 ... and 、

Agglomerative subgroup （Co⁃hesive Subgroup） Analysis is an important method in social network analysis , Its purpose is to reveal the actual or potential relationship between social actors . When the relationship between some actors in the network is particularly close , So that when combined into a sub group , Social network analysis calls such groups cohesive subgroups . If the network has coherent subgroups , And the density of agglomerative subgroups is higher , Explain this part of the congruent subgroup Actors They are closely connected , Frequent communication in information sharing and cooperation .

Four 、

The core — Edge structure analysis According to the degree of tightness between nodes in the network , The nodes in the network are divided into two regions , Core and edge areas . Nodes in the core area play an important role in the network , The core — The purpose of edge structure analysis is to study which nodes in the social network are in the core position , Which nodes are at the edge . The core of social network analysis method — Edge structure analysis can be applied to the network “ Location ” Quantitative analysis of the structure , Distinguish the core and edge of the network

eccentricity （Eccentricity）：

The distance from a given starting point to its farthest node .

Community Division ：

Clustering algorithm uses community detection （community detection） Algorithm , It is also called community discovery algorithm , It is a technology used to reveal network aggregation behavior . Community detection is actually a method of network clustering , there “ Community ” There is no strict definition in the literature , We can understand it as a set of nodes with the same characteristics . A big bull in the field of complex networks Newman A module is proposed （modularity） The concept of , Thus, the advantages and disadvantages of network community division can be measured by a clear evaluation index . So modularity actually refers to the difference between a network and a random network under a certain community division , Because random networks do not have a community structure , The greater the difference, the better the community division . Sometimes it is necessary to use the weight of edges when dividing modules , Generally speaking, for community discovery , The greater the edge weight , It should be said that the closer the connection between the two points , The easier it is to be divided into the same community In .

—— Collate in the network and related papers