It was almost two years back when I was sitting over a heap of data, trying to dig deeper and deeper with a hope to make some sense. Sometimes overwhelmed and sometimes depressing though, I was struggling hard trying to make sense of the way communities could behave, and wondering how and when an individual could furnish to become an influential player in the entire business of co-creating the much-needed content for meeting the informational needs of their own communities. It was then that I happen to hop on to the entirely new domain of social network analysis. Social network analysis is indeed ‘the buzz word’ in today’s world.
Human, we all know is a social animal and, is surrounded by a mesh of interactions interlacing together. A friendship network comprising college students or of friends on Facebook; a follower network formed on LinkedIn or Twitter, a spread network of health information sources or sources of rumour for that matter, befit well as visible examples of social networks. The way an individual acts and interacts, is often in accordance with the networks he/she happens to be part of. By merely exploring the relational data using a set of social entities like the people themselves or groups they are a part of or an entire organization; with some relationships or interactions happening between them, one can actually construct a social network. The underlying structure so formed, is the object of study of Social Network Analysis (SNA) and the methods and techniques thus designed help unearth the interaction patterns between these social actors within a given social network. It is though important to remember that it is not just the personal attributes of an individual/ social actor that basically tells us what is happening at the individual level or at the societal level, rather it is the place of an individual in a network that serves as an important indicator to understand how a particular individual is behaving in a the particular manner and how he/she is contributing to the social reality that we want to study. Based on the relational sociology framework, it is the positional existence of an individual/actor with respect to the other positions in a network that forms the focus of SNA.
SNA characterizes societies as networked societies with individual actors, people, groups or organizations forming “nodes” and the relationships or interactions between them as forming “ties/edges or links” connecting these nodes. The networks so formed are often visualized through ‘sociograms’ with nodes being represented as points and ties being represented as lines as shown in the figure below:
Scope of SNA cuts across various disciplines right from its utilization in hard core scientific fields like computer science, medical & biological studies to interdisciplinary fields of social science. To add further, use of SNA to conduct gendered analysis with the overarching themes of empowerment and equity has immense value. With social structures disproportionately influencing the opportunity structures for women, use of SNA can aid in facilitating comprehension about distribution of resources and directional influences that flow through and between the different social ties, existing both at the micro as well as the macro level. While at a micro level, SNA can provide us with an analysis of various individualistic aspects; at a macro level, it can provide us with a host of factors operating at the familial, community or institutional level adding to our understanding of how these factors affect behavior, choices, opportunities available to women. For example, Dr. Shyam Singh from IRMA, in his study of the Dairy Cooperatives of AMUL, India, utilized SNA to provide insights on how use of SNA has helped map participation of women in dairy-enabled networks in India. In one of the coffee break sessions organized by GENSA, Dr. Shyam explained how these networks are helping women in negotiating specific issues related to social mobility and decision-making. The use of SNA helped in drawing inferences in terms of how and to what extent participation of women in dairy cooperatives (or any collective action) has benefitted these women. In another example of a study looking at role of community radio in influencing perceptions of community men and women towards women’s health issues deeply entrenched in socio-cultural norms and beliefs, use of SNA helped in identifying the most influential sources of health information accessed by rural women living in the Tehri-Garhwal district of Uttarakhand, India eventually leading to the designing of a successful health communication intervention.
So, while trying to investigate key thematic areas like relative gender inequalities, social norms and cultural community perspectives, role of social capital in creating opportunities for marginalized women, potential of formal groups like Self-Help Groups (SHGs), Cooperatives, or even informal groups, SNA can serve as an effective means of analysis.
The visualizations so formed serve as means of qualitatively assessing a network. The visual representation of the nodes and edges reflect different attributes of interest. Identification of most influential/ prestigious or central actor(/s) and of hubs (of authorities/power) and how information spread takes place within a network are Common tasks SNA performs and, Various statistical measures, link analysis algorithms and diffusion algorithms are utilized for interpreting these. It is be noted that if we are interested in doing a network study, like the ones discussed above, one needs to include simple close-ended questions. For conducting SNA, remember that the data gets collected as a binary data & there are n by n matrices formed which can be visualized using software like UCINET, Gephi Cytoscape, NodeXL, Pajek, NetMinor & R. Interestingly, even using one network question will give you ample data to look at multiple attributes like density, reciprocity, and centrality. Added to this, one may consider conducting in-depth interviews to get answers to ‘why’ questions and can collate the responses to gain holistic insights into the types of relationships existing within a community and the nature of those relationships. The kind of data you get will help in not only understanding the dependencies between various social actors, but will also aid in characterizing their behavior and their effect on the network as a whole.
The field of SNA is still burgeoning with possibilities in terms of contributing insights into varying research interests but still lack efficiency. The current and future trends of utilizing SNA include new emerging lines of applicability spread across varying domains. This article paves the way for introducing the concept to initiate discussions on its use in solving much more complex problems of the world.