ICTACT Journal on Soft Computing

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Feature Based Community Detection by Extracting Facebook Profile Details


Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, India
     

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The rise of social networks had marked the revolution and transformation of human relationships and the information age. Social networks, Facebook in specific, have more than a billion daily active users which means petabytes of data are generated every second and there are so many social interactions occurring simultaneously. Community detection revolves around the study of these social interactions and common interests to derive the most efficient method of communication to specialized groups. Considering a preferred set of features such as the posts, likes, education background and the location of users for an optimal data structure, the selection of significant users for community analysis is implemented with the unique approach to investment score and dynamic threshold allocations for the graph creation. The community detection process focuses on the analysis of cliques and map-overlay. The emphasis on the detection of overlapping communities enhances the analysis of community relationships.

Keywords

Community Detection, Data Structure, Link Weights, Influence Metric, Cliques, Map Overlay.
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Abstract Views: 296

PDF Views: 3




  • Feature Based Community Detection by Extracting Facebook Profile Details

Abstract Views: 296  |  PDF Views: 3

Authors

Rajeswari Sridhar
Department of Computer Science and Engineering, Anna University, Chennai, India
Akshaya Kumar
Department of Computer Science and Engineering, Anna University, Chennai, India
S. Bagawathi Roshini
Department of Computer Science and Engineering, Anna University, Chennai, India
Ramya Kumar
Department of Computer Science and Engineering, Anna University, Chennai, India
Sundaresan
Department of Computer Science and Engineering, Anna University, Chennai, India
Suganthini Chinnasamy
Department of Computer Science and Engineering, Anna University, Chennai, India

Abstract


The rise of social networks had marked the revolution and transformation of human relationships and the information age. Social networks, Facebook in specific, have more than a billion daily active users which means petabytes of data are generated every second and there are so many social interactions occurring simultaneously. Community detection revolves around the study of these social interactions and common interests to derive the most efficient method of communication to specialized groups. Considering a preferred set of features such as the posts, likes, education background and the location of users for an optimal data structure, the selection of significant users for community analysis is implemented with the unique approach to investment score and dynamic threshold allocations for the graph creation. The community detection process focuses on the analysis of cliques and map-overlay. The emphasis on the detection of overlapping communities enhances the analysis of community relationships.

Keywords


Community Detection, Data Structure, Link Weights, Influence Metric, Cliques, Map Overlay.

References