A Hybrid Heuristic Community Detection Approach

Community detection is a very important concept in many disciplines such as sociology, biology and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks. In fact, the analysis of this data make it possible to extract new knowledge about groups of individuals, their communication modes and orientations. This knowledge can be exploited in marketing, security, Web usage and many other decisional purposes. Community detection problem $(C\mathcal{DP})$ is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper we propose a hybrid heuristic approach that does not need any prior knowledge about the number or the size of each community to tackle the $C\mathcal{DP}$. This approach is evaluated on real world networks and the result of experiments show that the proposed algorithm outperforms many other algorithms according to the modularity $(\mathcal{Q})$ measure.