Faculty Publications

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Publications by NITK Faculty

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Subject: search.filters.subject.Community detection

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    Community detection using meta-heuristic approach: Bat algorithm variants
    (Institute of Electrical and Electronics Engineers Inc., 2017) Sharma, J.; Annappa, B.
    In the present world, it is hard to overlook - the omnipresence of 'network'. Be it the study of internet structure, mobile network, protein interactions or social networks, they all religiously emphasizes on network and graph studies. Social network analysis is an emerging field including community detection as its key task. A community in a network, depicts group of nodes in which density of links is high. To find the community structure modularity metric of social network has been used in different optimization approaches like greedy optimization, simulated annealing, extremal optimization, particle swarm optimization and genetic approach. In this paper we have not only introduced modularity metrics but also hamiltonian function (potts model) amalgamated with meta-heuristic optimization approaches of Bat algorithm and Novel Bat algorithm. By utilizing objective functions (modularity and hamiltonian) with modified discrete version of Bat and Novel Bat algorithm we have devised four new variants for community detection. The results obtained across four variants are compared with traditional approaches like Girvan and Newman, fast greedy modularity optimization, Reichardt and Bornholdt, Ronhovde and Nussinov, and spectral clustering. After analyzing the results, we have dwelled upon a promising outcome supporting the modified variants. © 2016 IEEE.
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    A novel two-step approach for overlapping community detection in social networks
    (Springer-Verlag Wien michaela.bolli@springer.at, 2017) Sarswat, A.; Jami, V.; Guddeti, G.
    With the rapid increase in popularity of online social networks, community detection in these networks has become a key aspect of research field. Overlapping community detection is an important NP-hard problem of social network analysis. Modularity-based community detection is one of the most widely used approaches for social network analysis. However, modularity-based community detection technique may fail to resolve small-size communities. Hence, we propose a novel two-step approach for overlapping community detection in social networks. In the first step, modularity density-based hybrid meta-heuristics approach is used to find the disjoint communities and the quality of these disjoint communities can be verified using Silhouette coefficient. In the second step, the quality disjoint communities with low computation cost are used to detect overlapping nodes based on Min-Max Ratio of minimum(indegree, outdegree) to the maximum(indegree, outdegree) values of nodes. We tested the proposed algorithm based on 10 standard community quality metrics along with Silhouette score using seven standard datasets. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art works in terms of quality and scalability. © 2017, Springer-Verlag GmbH Austria.