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Analyzing and labeling telecom communities using structural properties

Analyzing and labeling telecom communities using structural properties,10.1007/s13278-011-0020-1,M. Saravanan,G. Prasad,S. Karishma,D. Suganthi

Analyzing and labeling telecom communities using structural properties   (Citations: 3)
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Social network analysis and mining has been highly influenced by the online social web sites, telecom consumer data and instant messaging systems and has widely analyzed the presence of dense communities using graph theory and machine learning techniques. Mobile social network analysis is the mapping and measuring of interactions and flows between people, groups, and organizations based on the usage of their mobile communication services. Community identification and mining is one of the recent major directions in social network analysis. In this paper we find the communities in the network based on a modularity factor. Then we propose a graph theory-based algorithm for further split of communities resulting in smaller sized and closely knit sub-units, to drill down and understand consumer behavior in a comprehensive manner. These sub-units are then analyzed and labeled based on their group behavior pattern. The analysis is done using two approaches:—rule-based, and cluster-based, for comparison and the able usage of information for suitable labeling of groups. Moreover, we measured and analyzed the uniqueness of the structural properties for each small unit; it is another quick and dynamic way to assign suitable labels for each distinct group. We have mapped the behavior-based labeling with unique structural properties of each group. It reduces considerably the time taken for processing and identifying smaller sub-communities for effective targeted marketing. The efficiency of the employed algorithms was evaluated on a large telecom dataset in three different stages of our work.
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    • ...In fact, Facebook has surpassed Google to be the most popular website in the world in March 2010 (Dougherty 2010); online social networks have also raised special interest among commercial businesses, medical and pharmaceutical companies as a channel to influence the opinion of their customers (Bhattacharyya et al. 2011; Kayaalp et al. 2011; Rosen et al. 2011; Saravanan et al. 2011)...

    Wei ZhangWeiliet al. Positive influence dominating sets in power-law graphs

    • ...Analyzing the structure of the networks has motivated an intensive academic research with topics like the detection of communities (Blondel et al. 2008; Saravanan et al. 2011), or the inference of hierarchical structures (Clauset et al. 2006)...
    • ...Kayaalp et al. (2010) use a combination of collaborative filtering and content filtering as an hybridized filtering system while Esslimani et al. (2010) make use of behavioral networks...
    • ...On the community detection side, Saravanan et al. (2011) perform community identification as well as labeling the units of the network by analyzing their structural properties...

    Stéphane Peterset al. Iterative Multi-label Multi-relational Classification Algorithm for co...

    • ...The analysis of the ‘social’ aspects of a network is the study and exploitation of the structural information present in the network, such as existence and strength of communities (Saravanan et al. 2011), node centralities, network robustness to node removal, topology evolution over time (Gilbert et al. 2011) and so on...

    Leandros A. Maglaraset al. New measures for characterizing the significance of nodes in wireless ...

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