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

Mapping Networks of Terrorist Cells

Krebs, V.E. (2002).

Summary
Through this article, Krebs attempts to map the social networks of the 19 hijackers involved in the terrorist attacks of September 11, 2001. He uses the same approach as he does for mapping project teams within organizations; however, while overt networks can be fairly easy to map, covert networks are significantly more difficult.

Krebs cites Malcolm Sparrow (1991) in describing three major problems of analyzing criminal networks:

1. Incompleteness - the inevitability of missing nodes and links that the investigators will not uncover
2. Fuzzy boundaries - the difficulty in deciding who to include and who not to include
3. Dynamic - these networks are not static, they are always changing. Instead of looking for the presence or absence of a tie, Sparrow recommends looking at waxing or waning strength of ties based on the situation.

These terrorist networks are held together by deep, trusted ties that are usually not visible to outsiders. Ties are strengthened when terrorists spend time together, specifically in classes or training. 

Krebs' initial social network (Figure 1) was created using strong ties built between terrorists who lived and learned together. Interestingly, many of the individuals on the same flight were more than two steps away from each other (beyond the horizon of observability). This practice minimizes the damage to a network if an individual is captured or compromised. Interestingly enough, Osama bin Laden reiterated this strategy: "Those who were trained to fly didn't know the others. One group of people did not know the other group" (Department of Defense, 2001). 

Figure 1. Trusted Prior Contacts

This initial network poses the question how work gets completed if members are so unconnected? Essentially, meetings are held which develop short-cut ties to coordinate between distant nodes in the network (Figure 2). However, after these meetings are held, the short-cut ties fall dormant until the need for activity again. These shortcuts improve the overall information flow in a network.

Figure 2: Trusted Prior Contacts + Meeting (Short-cut) Ties

Mohamed Atta, the hijacker's ring leader, scored the highest on measures of degrees and closeness, and was second in betweenness centrality. These measures do not necessarily identify Atta as the leader, but there are likely a number of nodes and links absent from these maps. Figure 3 includes contacts and intermediaries who assisted the 19 hijackers and clearly shows Atta at the center of the 'Hamburg cell' (in the bottom left) and his importance to the overall network.

Figure 3: Hijacker's Network Neighborhood

Social network analysis is frequently used for prosecution; once the investigators have a suspect, they can map the criminal's network through financial transactions, phone records, messaging services, and other types of government records. Attempting to prevent illegal activities based on social network analysis is much more difficult. Covert networks limit their outside ties and keep their internal, strong ties dormant unless necessary. Reducing overall number and activity of ties reduces the visibility of the network and minimizes the possibility for a leak.

Interestingly, many of the strong ties in Figure 3 were concentrated around the hijackers trained as pilots. This concentration of both unique skills and connectivity within the same nodes makes a network very vulnerable to disruption. These key individuals can be targeted by law enforcement for capture or compromise and severely deteriorate the operational and logistical capabilities of the covert network. 

While this knowledge could be used to prevent illegal activities by covert networks, the challenge still lies in identifying members of the network before it is too late. In order to overcome this hurdle, Krebs recommends that the various intelligence agencies share and aggregate their information to build a larger, more complete network. There are significant difficulties in putting this recommendation into practice, but it would be an advantageous first step to discovering and disrupting covert networks.

Critique

The article presents a detailed view of how social network analysis can be used to assess and take action against covert networks. Krebs' analysis that the most connected members of this network were also those with the unique skills (the pilots) is something that can be applied to similar terrorist network structures in order to prevent their activities. One improvement that should be made, or a measure that can be considered in future research, is the use of Eigenvector centrality. Eigenvector centrality is used to determine the influence of nodes in a network through their connections to other high or low scoring nodes. This measure would have likely shown Mohamed Atta to be the leader of this particular social network.

Source:

Krebs, V.E. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43-52.

Retrieved from: https://www.aclu.org/sites/default/files/field_document/ACLURM002810.pdf 

Morphological Analysis (Rating 3.5 out of 5 Stars as a Method; 4.25 out of 5 stars as a Modifier)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the  articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in November 2015 regarding Morphological Analysis as an Analytic Technique specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

Morphological Analysis (MA) can be used as either a methodology or a modifier. According to Fritz Zwicky, the developer of Morphological Analysis, this is a method for structuring and investigating the relationships between components and dimensions of a complex problem. This methodology allows an analyst to eliminate potential relationships due to inconsistencies between components. A less-structured version of MA can be used as a modifier through idea generation, mental modelling, or brainstorming (Refer to Structured Analytic Techniques for Analysis by Heuer and Pherson).

Strengths:

• Helps identify low probability-high impact scenarios
• Can weigh and consider many variables and scenarios
• Identifies intelligence gaps and helps form the intelligence collection plan
• Leaves an audit trail about how judgments are reached
• Can be used for idea generation, mental modelling, or brainstorming
• Can generate scenarios either intuitively or through quantifiable software
• Highly flexible, can be used in a variety of fields
• Can be updated and adjusted with new information
• Reduces the chance events will play out in a way that the analysts has not previously imagined

Weaknesses:

• It has potential for personal bias to affect the matrix
• The structure could inhibit free thinking
• When used as a method, there is a high difficulty in determining a definitive estimate
• Can generate too many possibilities that may distract analyst
• An exhaustive approach will quickly grow to involve a large number of scenarios, requiring computer assistance
• When used as a modifier, there is not a large amount of support in the literature

How-To:

• Step 1: Determine the objective or situation that needs to be analyzed or defined
• Step 2: Identify and properly define the dimensions of the problem – that is to say, the relevant issues involved.
• List the things about the situation that can be varied or changed in some way. Select a subset of two to six variables to investigate further. These will normally be significant parts of the situation
• Step 3: For each issue (parameter), a spectrum of “values” must be defined. These values represent possible, relevant states or conditions that each parameter can assume.
• Reduce the total set of (formally) possible configurations in the problem space to a smaller set of internally consistent configurations representing a “solution space”.
• Evaluate the solution space to assess relative probabilities of the remaining scenarios.
• In an idea generation modifier of this technique, plotting the criteria and the corresponding situations is the primary step. Then, visually matching the corresponding scenarios can assist in discovering new ideas and assist in further analysis.

Personal Application of Technique:

For the in-class exercise, a less-structured version of MA was used as an idea generation technique. A PowerPoint presentation was produced defining Morphological Analysis, application, value and included instructions for the class exercise. For the class exercise, all students were paired up into groups of two and worked together on Google Docs. A matrix on Google Docs was constructed with predefined dimensions: Group, Type of Attack, Target, and Impact. The scenario the groups explored was: A Terrorist Attack in Erie. Each group filled out the matrix with criteria matching the dimensions. The second step involves generating scenarios, intelligence gaps and low-probability high-impact events based upon on the ideas generated within the matrix. Each category in the second step was separated to give room for the groups to type out their answers.

For Additional Information:

Ritchey, T. (1998). General Morphological Analysis. Swedish Morphological Society. Retrieved from: http://www.swemorph.com/pdf/gma.pdf

Swedish Morphological Society

"Using Morphological Analysis To Solve Complex E-Learning Problems"
By: John Aleckson
Web Courseworks: a learning technologies company

Summary:
 The scenario begins by presenting a situation in which a business has multiple decisions and scenarios to deal with. Possible questions that may need to be answered: How do you compensate Subject Matter Experts (SMEs)?, Do you hire full-time employees or do you outsource?, etc, etc. All of these are issues facing businesses where analytical methods could assist in the decision making process.

In this article, the author selects morphological analysis to use in this selected scenario. The benefit of morphological analysis is that it allows the analyst to look at an issue from many different angles and allows for the discovery of different and unique details of a problems. In the example the author uses a situation in analyzing how a business should go about hiring a SME.

As you can see, the general issue at hand is how to hire a SME. The two overarching factors are SME Compensation and SME Involvement in the project. There are then 3 levels of commitment criteria to analyze and 4 factors of compensation to input into the matrix and analyze. Depending on the resources available and the exact project at hand, you can then select the best or the top few SME situations that will fit your current projects/scenario.

Critique:
The main criticism of this is that it does not go into detail as to how you select the various factors to input into the matrix. Similar to the issues of MCIM, there still is the potential for bias when it comes to "weighting" and deciding what to input into each box. If an analyst already has a preconceived idea of the best plan this method's usefulness will be severely weakened. The author mentions and I agree, this method is best when a group of people work on it. If only one person works on it the advantage of idea generation is definitely weakened as well.

Source:
http://www.webcourseworks.com/using-morphological-analysis-solve-complex-elearning-problems/