Using node centrality to understand network dynamics

One of the most helpful aspects of social network analysis, to me, is its use of visuals.  Being able to literally see a picture of a network helps me understand patterns and relationships in a way that neither numerical or textual descriptions can.  Eyeballing the graph is helpful, but social network metrics give us an even deeper picture.  Node centrality measures (betweenness, degree, closeness, and eigenvector) describe specific characteristics of the relationship between nodes.  This infographic briefly describes what these measures can tell us about the network.

(c) Beehaus

Social network analysis is very flexible and can be used to study many different types of networks.  SNA is a promising, albeit underused, methodology in understanding bystander intervention.  Research has shown that norms supporting prosocial bystander behavior increase people’s intent to help, while norms against intervening decrease intent to help (Hoxmeier, Flay, & Ackok, 2016).  Researchers have not, however, used SNA to look at how the group norms are created and spread in the first place.  People who are high in closeness centrality spread new information quickly and efficiently; they could be identified and targeted for specific training.  Identifying people high in betweenness centrality would help prevention educators strategically interject prosocial norms to more isolated parts of the network.  As opposed to training as many people as possible, SNA could help educators efficiently target programming where it would do the most good.

Researchers have used SNA to study gender-based violence response networks.  For example, Rana and Allen (2015) compared the networks of organizations in five separate family violence councils.  They found that the prominent organizations were different in each council.  For example, the domestic violence program was clearly the most important organization in councils A and C as it was highest in all three centralities.  In council D, the prominence of the domestic violence program depended on the role being investigated.  It was highest in betweenness, meaning it was the key broker/bridge in the network.  Child and family services was highest in degree and closeness, meaning it was connected to more organizations and involved in more relationships between organizations.  The researchers discussed the findings in relation to leveraging relationships between organizations to further council goals and initiatives.  The SNA approach allowed for more nuance in understanding the relationships than other analyses would have.

Quinlan and Quinlan (2010) used SNA in a much different way.  Instead of looking at people or organizations, they analyzed institutional and lived experiences of rape.  In the institutional network, the nodes represented the components of the forensic medical exam.  They included physical evidence, survivor history, assault details, and medical professionals.  The links between nodes represented the connections between aspects of the exam.  The lived experience network, however, set the survivor’s feelings, actions, and thoughts as the nodes in the network.  Time (order of what happened when) was represented by the links.

The researchers used node centrality measures to determine the most prominent components of each network.  For the lived experience network, the survivor’s feelings of fear and horror had the highest degree centrality.  This suggested that for this survivor, those feelings were the most powerful aspects of her experience.  For the institutional network, the survivor’s identity had the highest degree centrality and the assault had second highest.  This suggested that the survivor’s identity was more central than any other aspect of the exam process.  In the discussion, the researchers related these findings to the ways that rape is viewed and treated in society.  The use of SNA gave a different and compelling perspective on rape and sexual violence research.



Hoxmeier, J. S., Flay, B. R., & Ackok, A. C. (2016). Control, norms, and attitudes: Differences between students who do and do not intervene as bystanders to sexual assault. Journal of Interpersonal Violence, 1-23, doi: 10.1177/0886260515625503

Quinlan, E. & Quinlan, A. (2010). Representations of Rape: Transcending Methodological Divides. Journal of Mixed Methods Research, 4, 127-143.

Rana, S. & Allen, N. E. (2015). Centrality measures to identify key stakeholders in Family Violence Councils. Psychosocial Intervention, 24, 167-176.

Social capital and social networks

Social capital refers to the resources available through relationships and social networks.  It can be built, invested, transferred, and spent.  

Social capital diagram
(c) Coact 2015

One of the most well-known discussions of social capital is in Robert Putnam’s article (1995) and subsequent book (2000), Bowling Alone.  In the book, Putnam explores what he saw as the decline of social capital in the U.S., and the subsequent negative impacts on communities and individuals.  Note the timing of the article and the book – while the internet already existed when Putnam wrote them, it was not as ever-present as it is today.  Cell phones were still used primarily for emergencies and dial up modems were the primary way most people accessed the internet.  The chart below shows the growth in internet and mobile technology usage since 2000.

It is unlikely that Putnam, and other social scientists, could have even imagined the role that the internet and internet-related technology would play in the building and transmission of social capital today.  Current research has tried to tease out that relationship.  While the question of whether the internet increases or decreases people’s social capital has not been definitively answered (see here and here for examples), many researchers agree that its prevalence in our lives has changed how social capital is built and valued.  As Charles Steinfield said, we are not bowling alone, we are bowling online.

Social capital impacts all facets of our lives, from community resilience to health outcomes. Individual social capital is an important, but not often discussed, part of gender-based violence prevention.  Bystander behavior is very much embedded in people’s social networks – network norms shape people’s attitudes and behaviors in ways that can support and/or oppose violence and abuse.  In networks where social norms are tolerant of abuse, individuals who speak out against it pay a price in social capital.  Members of the network who have more social capital (from status, prestige, or other valuable characteristics) can afford to take risks and lose social capital in ways that members with less social capital cannot.  This makes the decision making process of whether or not to intervene, and in what way to intervene, different for those with high versus low social capital.

People who occupy prominent roles in the network and have high social capital are also more effective at changing the group norms around bystander intervention than those with low social capital.  This is why prevention programs such as Green Dot and Huddle Up target high status individuals.  To my knowledge, social network analysis has never been a part of the evaluation of these types of programs.  Although methodologically challenging, it would be helpful to see how bystander intervention information actually spreads through sample networks.  Were the individuals and groups who were identified as high status by the program organizers (usually administrators or professional staff) the ones who 1) had high social capital and 2) were willing to activate their social capital for this issue?  

Social capital is also an important area of research around institutions’ response to gender-based violence. Research that answers the following questions would provide critical information to improve institutional response to survivors.  Does social capital impact survivors’ choices report assaults or participate in institutional or criminal hearings? If so, how?  What do response systems look like at institutions where advocates have high social capital versus where they have low social capital? How does social capital impact collaborations between departments, and what impact does that have on institutional response?

Is it a small world, after all?

Two weeks ago, I blogged about Six Degrees of Kevin Bacon.  At the time, I used the game as an example to introduce the concept of social network analysis in general.  This week, I am referring back to it as an example of a specific social network principle: the small world theory.  The small world theory, made famous by Stanley Milgram’s research, posits that people can be connected to each other in less than 6 degrees.  The Six Degrees of Kevin Bacon game replicated that theory within the acting world.  While you may not know if you are connected to Kevin Bacon, you have likely encountered a stranger whom you later learned you were connected to in some way.  For me, this happened for the first time in the 7th grade.  I had started at a new school, and we had a project to trace our family trees back as far as we could.  The social studies teacher had done this project for many years, and had found familial connections between students who did not know they were related.  He discovered that I was distantly related to two of my friends (and Warren Beatty)!

My distantly related childhood friends

Before we dive too much into the small world theory, let’s review some key SNA concepts.

Nodes (sometimes called actors) represent individuals, organizations, or other entities.

Ties are the links between two actors in a social network; ties are also referred to as links or relationships.

The purpose of SNA is to describe the patterns of relationships (ties, links) between nodes (individuals, organizations).


The strength of ties is dependent on a variety of factors, including the type of network, the research question, and the important relationship characteristics in the analysis (Kadushin, 2012).  In general though, strong ties are relationships where people have multiple, important, deep, or long connections to each other.  Weak ties, on the other hand, are much easier to break and may be based on fewer or more superficial characteristics.  Stronger does not mean better in the context of networks, however.  Weak ties are key to the functionality of the small world theory.  Nodes with large numbers of strong ties tend to be insulated within their own network and disconnected from people in other networks.  As a node’s number of weak ties increases, so does the number of networks to which it can belong.  In his often-cited article on weak ties, sociologist Mark Granovetter (1973) wrote that weak ties serve as bridges between network clusters.

A weak tie serves as a bridge

Resources (such as information, behaviors, or money) are able to spread throughout the larger network due to these weak ties.  Without weak ties, there would be no small world phenomenon.  There would only be a big world, consisting of many separate networks with nothing passing between.  In this regard, weak ties are integral to network diffusion.

Also, without weak ties, my Bacon number would not be 3!

images from

SNA and My Bamboo Infestation – How Tracking Unseen Connections Leads You to the Bigger Picture

My backyard is infested with bamboo.  The people who owned the house before us thought it would be a great idea to plant bamboo in the backyard (it does, after all, have many helpful environmental properties).  They neglected to fully account for the growing patterns of bamboo, and when we moved in it had choked out much of the grass.  As bamboo newbies, we tried to get rid of it by chopping down the mature bamboo and by knocking down new shoots as they came up.

We thought we were dealing with this kind of problem:

bamboo shoots at ground level
Image from

We were actually dealing with this kind of problem:

The root system of bamboo
Image from

Social network analysis operates on the assumption that human behavior is like running bamboo – what you see on the surface (individuals or bamboo shoots) is connected by a complicated path that run just below the surface (relationships between people or the root system of the bamboo plant).  Addressing what is visible is not as effective as addressing the entire system.

Social network analysis (SNA for short) is an integrated way to understand how relationships and interactions impact all facets of our lives (Robins, 2015).  Traditional social science research generally centers on individuals, groups, or systems.  Gender-based violence researchers, for example, often focus on increasing bystander behavior (individual), groups that are at higher risk for perpetration or victimization (groups), and rape culture (systems).  These lines of research are important and have shaped a great deal of current response and prevention work.  Some research questions, however, cannot be effectively answered through traditional methodologies.  For example, why might bystander intervention be more prevalent on some college campuses than others?  Measuring bystander behaviors can tell us there is a difference, but not much more.  Focusing on individuals, groups, or institutional levels will not help us understand the mechanisms that lead to different behaviors in different places.

To do this, we need to change our perspective.

Perspective GIFs - Find & Share on GIPHY

Instead of having individuals, groups, or systems as the center of analysis, SNA looks at the relationships (connections) between them.  These connections form networks, and it is the structure and nature of these networks that are analyzed.  According to Yang, Keller, and Zheng (2017), SNA uses two datasets instead of one.  The node dataset is similar to what is used in other social science research – it includes the individuals or organizations (called nodes in SNA).  The second dataset holds the relationship information – how the nodes are connected to each other.  SNA offers the unique capability to analyze the two types of data together.  This analytic shift offers a more nuanced, dynamic picture of how information, behaviors, norms, and even goods spread throughout networks.

Tracking back to our example above, traditional gender-based violence research may ask people to list the number of times that they have intervened in potentially abusive situations.  SNA, however, would ask people to list those in their social circles who have intervened in abusive situations, people they have talked to about bystander intervention, or people whose opinion on intervening is important to them.  Often we assume (or assert) that social groups have influence over people’s behaviors.  SNA actively investigates those connections to understand for whom and in what ways the influence occurs (Robins, 2015).

Connection GIFs - Find & Share on GIPHY

This relational data is what makes SNA powerful.  It also, however, makes it hard to predict anything.  Experimental studies are the most valid ways to predict outcomes because they control for as many variables as possible except for the ones in question.  SNA does not control for or exclude these other variables, it welcomes them in to the analysis!  In their book, Yang, Keller, and Zheng (2017) review new SNA designs that increase the predictive validity of the research.  The strength of SNA, however, still lies in its ability to tease out and describe network behavior.


Information cited above:

  • Robins, G. (2015). Doing social network research: Network-based research design for social scientists. Los Angeles, CA: Sage Publications.
  • Yang, S., Keller, F. B., & Zheng, L. (2017). Social network analysis: Methods and examples. Los Angeles, CA: Sage Publications.


Organizational Learning [] Learning Organization

The terms “organizational learning” and “learning organization” are mirror terms – they are comprised of the same words that are ordered in reverse.  Aside from making for interesting word play, the reciprocal relationship between the terms speaks to their underlying meanings. Organizational learning is a verb – the process of how organizations learn. Learning organization is a noun – an organization that learns.

Image result for infinity symbol

Nancy Dixon (1999) points out that organizational learning has been conceptualized by multiple researchers in multiple ways.  Some have very specific definitions and some have broad sketches.  They all share, however, concepts related to agency, change, and growth.  I resonate most strongly with Dixon’s discussion of organizational learning as the construction and reconstruction of meaning in a dynamic process.  In this view, knowledge and information are separated from learning and individual learning is separated from organizational learning.  Information only becomes useful when we integrate and  make sense of it to form knowledge.  When we take action on knowledge to adapt, grow, or change – then we are learning.  Dixon also points out that individual learning is not the same as organizational learning, even when multiple individuals in an organization learn.  Organizational learning requires a collective effort to share learning processes and outcomes in an integrated way.

The presence of learning does not necessarily lead to a learning organization.  Learning organizations make intentional efforts to engage in learning activities.  They have their eye on change.  They value new knowledge, meaning making, and growth.  They engage in the recursive process of learning, change, learning, and change.

In other words, they see themselves in both sides of the mirror.

Christakis and Fowler’s “Connected” explains the power of social networks

When I began thinking about social network analysis, the “Six Degrees of Kevin Bacon” game was the first mental association I made.  The object of that game is to pick an actor and trace their connection to Kevin Bacon through movie co-stars.  The folklore of the game is that every actor can be associated to Kevin Bacon by looking at chains of shared co-stars.

The shortest path between Emma Watson and Kevin Bacon is 2, since both starred with John Cleese
(c) Randy Olson

The game was created over 20 years ago, before websites like IMDB made it easy to track even the most obscure roles.  The advent of the internet has allowed people to create more detailed, intricate pictures of how actors are connected to Kevin Bacon, as well as to all other actors.  In an article for Business Insider, Randy Olson used IMDB to determine that actor Eric Roberts was connected to more actors than Kevin Bacon.  He found that 88% of all actors could be connected to Eric Roberts in three degrees or less, compared to 82% who could be connected to Kevin Bacon in the same amount.

Christakis and Fowler (2009) mentioned the Kevin Bacon game in their book Connected: How Your Friends’ Friends’ Friends’ Affect Everything You Feel, Think, and Do.  This book walks the reader through social network theory – the science of understanding the ways that people are connected.  Christakis and Fowler explained that relationships with people in our lives shape our attitudes, behaviors, and even health statuses. This assertion in and of itself was not groundbreaking. Christakis and Fowler went on to explain, however, that these parts of our lives are also impacted by people we may not even know – the friends of our friends’ friends.  The strongest influencers in our lives are those within three degrees of us in our social networks.

Happiness is contagious!!
Your kindergarten teacher was right – happiness is contagious.

The authors cited research and real world experiences to support social network theory.   They gave examples of how health issues, such as obesity and STDs, spread through social networks.  Even though STDs are communicable and obesity is not, the social network patterns were remarkably similar.  These patterns also showed up in happiness levels, voting behaviors, and how people found partners and jobs.

Technology has changed how we form networks.  It has increased the number of people with whom we can connect.  Christakis and Fowler found, however, that our offline and online networks tend to be similarly sized.  They point to evolution and genetics as factors that guide the size of our networks.  Sites such as Facebook increase the complexity of our social networks, but they do not replace our in-person networks.  The same rules apply in all social networks – we are all connected to each other, our behaviors and norms influence each other, and the most significant influences come from people within three degrees of separation.

Your friends’ friends’ friends