Expert talk on Social Network Analysis: Making the Invisible Visible


Over the past decade, there has been a growing public fascination with modern society’s complex & quot; connectedness,” especially since the emergence of social networking sites. Whether the rapid spread of news or the tipping point of social/political movements gathering momentum or the cascading of epidemics and financial crises around the world with alacrity and intensity, it is attributed to the connectedness of today’s society. Many scientific disciplines have come together and evolved into a new field called network science focused on understanding how these complex connected systems operate. Social Network Analysis (SNA) has emerged as an approach and a tool to uncover and understand the hidden side of such connections.

Social Network Analysis (SNA) is a line of inquiry that analyzes networks (a collection of objects and links that connect some pairs of these objects), their structures, and their dynamics by mining their data. Networks are everywhere—the internet, the world wide web, cellular networks, neural networks, satellite networks, and many more. Networks are also at the heart of many revolutionary technologies of the 21st century—Google, Facebook, Cisco, Twitter, Amazon, etc. These giant companies have helped generate humongous network data. The availability of big data sets—in volumes, greater variety, and velocity, coupled with the development of SNA software—Gephi, Pajek, NodeXL, Cytoscape and NetworkX, and R, to mention some popular ones, has also helped the robust growth of this fascinating field. By mapping and measuring the connections and flows between people, organizations, and other entities, SNA has found critical applications in many domains. It makes the invisible visible and helps make sense of our world and shape our preferences, predict, and nudge behaviour. This talk will introduce the basic concepts of Network Science, tracing its intellectual lineage to Graph Theory. It will touch upon the principles of Graph Theory that underpins network science and uses graph theory as a primary tool in the broader examination of networks. The talk will elaborate on attributes of Networks and their measures such as Centrality, Components, Cohesion, Geodesic, Density and Degree, Cores, Cliques, and others. Finally, with the help of examples from across different domains, this talk will illustrate how we could deploy SNA to understand and predict network behaviour. This talk will overview some select software such as Gephi, Pajek, NodeXL, Cytoscape, and NetworkX.


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