In this example, PSOmay not capture the true relationship between the members of the community. Weare using Zachary’s network of karate club members, a well-known dataset usedto test the performance of the communities. Fig 1(a) represents the originalcommunity in Zachary’s network. It is separated into two distinct communitiesin the network.
Fig 1(b) represents the resulting communities generated bytraditional PSO techniques. They generate superfluous small communities in thenetwork. So, it reduced the true relationship between the members in thenetworks. To improve this drawback, we use some optimization strategy andpost-processing strategy for merging the small-small communities.Comparedto the traditional algorithm, PSO based on the global search algorithm helps toanalyze the social behavior of swarms and it convergence faster.
It helps toavoid local optima and then get a more accurate result. So, PSO can producebetter result compared with the traditional algorithms.Themain advantage of this paper is 1) Discovering overlappingcommunities in a social network is equal to detecting the disjoint communitiesin the social network. 2) To find high-quality andfiner-grained overlapping communities from social networks we propose toinclude ensemble clustering into discrete particle swarm optimization. 3) To merge finer-grained andsuboptimal overlapping communities a novel post-processing strategy is used. The rest of the paper is organized as follows.Related works on overlapping community detection have been discussed in section2.
Definition and corollary have been discussed in section 3. Section 4discusses the details of our proposed work. The experimental setup has beendiscussed in Section 5.
Section 6 concludes the paper. II RELATED WORK 1. CommunityDetection in Social Networks Well-knownsocial media applications, such as Digg, Flickr and YouTube have createddifferent multimedia social networks, eager to performing community detectionon social networks. T. Mei et al 22 presented a description of the YouTubevideo-sharing virtual community. M. Wang et al 23 Proposed to deal with theproblem of disambiguates by applying community detection method to removecommunities hidden in the social networks.
J.Xie S.Killey 30 they use a multi-stage algorithm based on thelocal-clustering methodology for content detection.
A. Amelio and C. Pizzuti28 Proposed to detect communities in the networks they use hypergraphmodeling which is different from LEPSO methodology; their goal to detectmeaningful communities in the networks.2. Overlapping Communities DetectionIn recent times, moreeffort to defining the methods of community detection focuses on finding theoverlapping communities in the networks. A.Amelio, C. Pizzuti and J.
J. Whang28 29 proposed the link clustering method have been successfully applied tooverlapping communities’ detection. To detect overlapping communities they uselink clustering method by partitioning links instead of nodes. The advantage oflink clustering is that it produces an overlapping subgraph of the originalgraph. J. B. Pereira 25 proposed to find the overlapping community modulesfor protein-protein they use line graph method.
Y. Y. Ahn, J. P. Bagrow, and S.
Lehmann 26 proposed a hierarchical agglomerative link clustering techniquethey group the links into related clusters. J. Li and Y. Song 27 fixing thethreshold in the real world applications can easily mislead the clusteringprocess and they detect poor overlapping communities in the networks.IIIDefinition and Corollary Inthis section, we discussed about the definitions, theorems, and corollaries inthe paper.
A graph consists of nodes (N) and edges (E).Defined as G = edge corresponds to the pair of vertices or nodes. If the vertexn N represent a set of vertices’ incidence to n, and degree of the vertex ornode deg (n).