Meta-heuristic multi-objective community detection based on users’ attributes

Community detection (CD) is the act of grouping similar objects. This has applications in social networks. The conventional CD algorithms focus on finding communities from one single perspective (objective) such as structure. However, reliance on only one objective of structure. This makes the algorithm biased, in the sense that objects are well separated in terms of structure, while weakly separated in terms of other objective function (e.g., attribute). To overcome this issue, novel multi-objective community detection algorithms focus on two objective functions, and try to find a proper balance between these two objective functions. In this paper we use Harmony Search (HS) algorithm and integrate it with Pareto Envelope-Based Selection Algorithm 2 (PESA-II) algorithm to introduce a new multi-objective harmony search based community detection algorithm. The integration of PESA-II and HS helps to identify those non-dominated individuals, and using that individuals during improvisation steps new harmony vectors will be generated. In this paper we experimentally show the performance of the proposed algorithm and compare it against two other multi-objective evolutionary based community detection algorithms, in terms of structure (modularity) and attribute (homogeneity). The experimental results indicate that the proposed algorithm is outperforming or showing comparable performances.