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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  • B. Pattabiraman et al., “Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection”, Journal of Internet Mathematics, Vol. 37, No. 1, pp. 156-169 2014.

  • Peng Gang Sun, “Weighting Links based on Edge Centrality for Community Detection”, Physica A: Statistical Mechanics and its Applications, Vol. 394, pp. 346-357, 2014.

  • Sudheendra Hangal, Diana MacLean, Monica S. Lam and Jeffrey Heer: “All Friends are Not Equal: using Weights in Social Graphs to Improve Search”, Proceedings of International Conference on Social Network Analysis Knowledge Discovery and Data Mining, pp. 356-371, 2010.

  • Mohsen Arab and Mohsen Afsharchi, “Community Detection in Social Networks using Hybrid Merging of Sub Communities”, Journal of Network and Computer Applications, Vol. 40, pp. 73-84, 2014.

  • Xingqin Qi, Wenliang Tang, Yezhou Wu, Guodong Guo, Eddie Fuller and Cun-Quan Zhang, “Optimal Local Community Detection in Social Networks Based on Density Drop of Subgraphs”, Pattern Recognition Letters, Vol. 36, pp. 46-53, 2014.

  • Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin and Ion Stoica, “Graphx: Graph Processing in a Distributed Dataflow Framework”, Proceedings of 11th Usenix Symposium on Operating Systems Design and Implementation, pp. 599-613. 2014.

  • W. Fan and A. Yeung, “Similarity between Community Structures of Different Online Social Networks and Its Impact on Underlying Community Detection”, Journal of Communications in Nonlinear Science and Numerical Simulation, Vol. 20, No. 3, pp. 1015-1025, 2015.

  • Gnce Orman, Vincent Labatut and Hocine Cherifi, “Comparative Evaluation of Community Detection Algorithms: A Topological Approach”, Journal of Statistical Mechanics: Theory and Experiment, Vol. 2, pp. 802-809, 2012.

  • Hans-Peter Kriegel, Thomas Brinkhoff and Ralf Schneider, “An Efficient Map Overlay Algorithm based on Spatial Access Methods and Computational Geometry”, Proceedings of International Workshop on Database Management Systems for Geographical Applications, pp. 194-211, 1991.

  • Mark De Berg, Marc Van Kreveld, Otfried Cheong and Mark Overmars, “Computational Geometry: Algorithms and Applications”, 3rd Edition, Springer, 2008.


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  • Feature Based Community Detection by Extracting Facebook Profile Details

Abstract Views: 230  |  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