Social Network Analysis: Introduction, Key Problems, ApplicationsSpeaker: Ramasuri Narayanam – Bangalore, India
Topic(s): Computational Theory, Algorithms and Mathematics
This lecture provides introduction to social network science and then proceeds to highlight key representative problems in this area. Finally, it presents several applications of social network analysis along with key resources to conduct research in this area. Below is a detailed description of the materials in this lecture.
Social networks are social structures made up of individuals (or autonomous entities) and connections among those individuals. These connections represent the patterns of communication among these individuals in networked systems. Social networks are usually modeled using graphs where nodes represent the individuals and the edges represent the relationship (such as friendship, co-authorship, citation, etc.) between these nodes. Recently, there has been a large surge of interest from the research community to study social networks (and in general network science) because of the following reasons:
(a) Such networks are fundamentally different from technological networks.
(b) It is observed that the degrees of adjacent vertices in networks are positively correlated in social networks unlike other networks;
(c) The level of clustering appears to be far greater than we expect by chance. And, the level of clustering in non-social networks is no greater than one would expect by chance.
(d) Networks are powerful primitives to model several real-world scenarios. A few such examples are friendship networks, co-authorship networks, world wide web, email networks, citation networks, trading networks, R&D networks, etc.
Social Network Analysis: The focus of a significant amount of research in Network Science is to understand the structural properties of social networks - for example degree distribution, average number of edges per node, density of edges, diameter of the network - and these studies are based on the tools and techniques from social network analysis (SNA). Essentially SNA helps for both studying the complex communication patterns among the individuals in the network and measuring the strength of relationships between the connected individuals. Apart from its extensive use in social sciences, SNA has been applied in areas ranging from biology, business organization, electronic communications, physics, psychology, etc. The existing research trends in SNA can be broadly classified into two major categories based on the granularity of information used in the specific approach:
(i) node/edge centric analysis, and
(ii) network centric analysis.
Below is a brief description of each of these two approaches:
Node/Edge Centric Analysis: Here the focus is on the design and analysis of metrics and measures targeted around the individual nodes/edges in the network. A few examples of this line of work include Centrality Measures, Link Prediction, and Anomaly Detection.
Network Centric Analysis: Here the focus is on modeling and analysis of the entire network together. A few important examples of this class of research include Community Detection, Frequent Subgraph Discovery, Graph Visualization, and Graph Summarization.
About this LectureNumber of Slides: 90
Duration: 90 minutes
Languages Available: English
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