Social Network Analysis - Theory and Applications

By: Dr. David L. Passmore - PennState

Summary:

A Social network is a social structure made up of individuals or organizations called “nodes”, which are connected by one of more specific types of inter-dependency, like friendship, kinship, common interest, financial obligations, sexual relationships, or beliefs.

Social network analysis (SNA) views social relationships in terms of network theory consisting of nodes and ties. Nodes are the individuals within the network, and ties are the relationships that connect the actors. There can be many kinds of ties between the nodes, and research has shown that social networks operate on a many different and complex levels. A social network is a map of specified ties between nodes being studied. The nodes to which an individual is connected are the social contacts of that individual. The network can also be used to measure social capital, the value that an individual receives from the social network. These relationships are often displayed in a social network diagram, where nodes are the points and ties are the lines.

SNA has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods, social network software, and researchers. Analysts reason from the whole to part, from structure to relation to individual, and from behavior to attitude. The power of social network analysis stems from its difference from traditional social scientific studies, which assume that it is the attributes of individual actors that matter. SNA produces an alternate view, where attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less room for the ability of individuals to influence their success. 

SNA has been used in a variety of research fields, and is an effective tool for mass surveillance. The Total Information Awareness program was doing in-depth research on strategies to analyze social networks to determine whether or not certain citizens were political threats. Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices.

Measures used in SNA:

Betweenness - The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node's neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links. 

Bridge - An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. 

Centrality - This measure gives a rough indication of the social power of a node based on how well they "connect" the network. "Betweenness", "Closeness", and "Degree" are all measures of centrality.

 

Centralization - The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses.

 

Closeness - The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. The shortest path may also be known as the "geodesic distance".

 

Clustering coefficient - A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness'.

 

Cohesion - The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.

 

Degree - The count of the number of ties to other actors in the network. 

 

Flow betweenness centrality - The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).
 

Eigenvector centrality - A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network   based on the principle that connections to nodes having a high score contribute more to the score of the node in question.

 

Local bridge - An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle.

 

Path length - The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.

  

Prestige - In a directed graph prestige is the term used to describe a node's centrality. "Degree Prestige", "Proximity Prestige", and "Status Prestige" are all measures of Prestige. 

Radiality - Degree an individual’s network reaches out into the network and provides novel information and influence.

 

Reach - The degree any member of a network can reach other members of the network.

Structural cohesion - The minimum number of members who, if removed from a group, would disconnect the group.

Structural equivalence - Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent.

 

Structural hole - Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.  

Example of SNL

 

Critique:

The source I used to summarize the article was extremely long, and I attempted to capture the basics of SNA to provide an introduction to the technique. I would recommend those interested to browse the PDF for more details, as the author describes different typologies of SNA, and provides a list of software that can be used.

In the intelligence field, SNA is extremely useful on the tactical level in a non-conventional military setting. I have unwittingly used this methodology downrange on i2. The way I applied SNA was by building connections to known Taliban members, which resulted in visual representations how several cells (groups) operated within our AOR (Area of Operations). Within each cell the leader was identified, and his connections to higher tier targets. With the information I was able to produce target packages, which our companies used to capture or kill HVTs (high value targets). The information I provided is vague (and sanatised), and other methods were used in conjunction with SNA, but were not described for OPSEC (operational security) purposes.

Source: http://train.ed.psu.edu/WFED-543/SocNet_TheoryApp.pdf