Dynamic community detection in evolving networks using locality modularity optimization

Abstract

The amount and the variety of data generated by today's online social and telecommunication network services are changing the way researchers analyze social networks. Facing fast evolving networks with millions of nodes and edges are, among other factors, its main challenge. Community detection algorithms in these conditions have also to be updated or improved. Previous state-of-theart algorithms based on the modularity optimization (i.e. Louvain algorithm), provide fast, efficient and robust community detection on large static networks. Nonetheless, due to the high computing complexity of these algorithms, the use of batch techniques in dynamic networks requires to perform network community detection for the whole network in each one of the evolution steps. This fact reveals to be computationally expensive and unstable in terms of tracking of communities. Our contribution is a novel technique that maintains the community structure always up-to-date following the addition or removal of nodes and edges. The proposed algorithm performs a local modularity optimization that maximizes the modularity gain function only for those communities where the editing of nodes and edges was performed, keeping the rest of the network unchanged. The effectiveness of our algorithm is demonstrated with the comparison to other state-of-the-art community detection algorithms with respect to Newman's Modularity, Modularity with Split Penalty, Modularity Density, number of detected communities and running time.

Evolving Networks and Social Network Analysis Methods and Techniques

“…Cordeiro et al [85] presented a modularity-based dynamic community detection algorithm. The algorithm is a modification of the original Louvain method where dynamically added and removed nodes and edges only affect their related communities.…”

<em>FbMapping</em>: AN AUTOMATED SYSTEM FOR MONITORING FACEBOOK DATA

“…This approach works only for a small area or local area. However, our work covers both local and global area [9].…”

“…Afterward, feature selection is used to select the most informative features for the original data set. Several techniques can be used for features selection from large data sets [9,18]. One of the powerful adaptive learning approaches for data clustering/feature selection techniques is the Linear Discriminate Analysis (LDA).…”

“…Every category had two sub-categories such as gender (male/female) and age (teenager/adult). Assume the number of users based on the two categories in different data records which checked automatically was as follows: (3,6), (4, 4)}, Age (C 2 ): x = (teenager x 1 , adult x 2 ) = {(9, 10), (6,8), (9,5), (8,7), (10,8)}.…”

Evolution properties of the community members for dynamic networks

“…Detecting community structures in dynamic networks [12][13][14][15][16] has attracted much attention. Palla et al [17] and Greene et al [18] developed models for tracking the evolution process of communities for dynamic networks, where each community is characterized by a series of significant evolutionary events, including growth, contraction, merging, splitting, birth and death.…”