OLCPM: An online framework for detecting overlapping communities in dynamic social networks

Abstract

Community structure is one of the most prominent features of complex networks. Community structure detection is of great importance to provide insights into the network structure and functionalities. Most proposals focus on static networks. However, finding communities in a dynamic network is even more challenging, especially when communities overlap with each other. In this article, we present an online algorithm, called OLCPM, based on clique percolation and label propagation methods. OLCPM can detect overlapping communities and works on temporal networks with a fine granularity. By locally updating the community structure, OLCPM delivers significant improvement in running time compared with previous clique percolation techniques. The experimental results on both synthetic and real-world networks illustrate the effectiveness of the method.

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

“…The second interesting finding is that authors seem to use various datasets for comparison, as they often include synthetic graphs and/or real-world graphs. In the assessed papers, 1 made use of only synthetic graphs [32], 32 used only real graphs [13, 14, 16, 22, 27, 28, 30, 31, 33, 34, 36-41, 44, 45, 48-52, 54-57, 59-63] and 17 used both [6,15,17,18,20,21,23,24,29,42,43,46,47,53,58,64,65]. In the 49 papers that used real graphs 47 different real graphs were introduced.…”

“…For the broad selection, the initial list of 51 papers on DCD methods was used [6, 13-18, 20-23, 27-65]. It was obtained by supplementing 32 temporal trade-off algorithms [6, 13-15, 17, 21, 22, 24, 27-50] from [1] with 19 algorithms not included in the aforementioned survey [16,18,20,23,[51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] that nonetheless possess interesting characteristics with regards to community and evolution extraction. Figure 1 illustrates the relevance of adding those 19 papers as it ensures the inclusion of more recent methods.…”

“…The approach is capable of singling out community evolution events such as birth, death, merge, split, growth and contraction. OLCPM is an online, deterministic and DCD method based on clique percolation and label propagation [18]. OLCPM, unlike CPM (Clique Percolation Method) [19], works by updating communities by looking at some predefined events resulting in improved computation times.…”

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

“…With the advent of complicated networks such as OSNs researchers are interested in designing CD algorithms for dynamic networks. In [35], the authors presented an online algorithm based on the clique percolation method (CPM) and label propagation algorithm, namely OLCPM. Their proposed algorithm works on a temporal network with fine granularity and tries to update the local community structure.…”

Detecting Stable Communities in Link Streams at Multiple Temporal Scales

“…Most of these methods consider that the studied dynamic networks are represented as sequences of snapshots, with each snapshot being a well formed graph with meaningful community structure, see for instance [12,5]. Some other methods work with interval graphs, and update the community structure at each network change, e.g., [17,3]. However, all those methods are not adapted to deal with link streams, for which the network is usually not well formed at any given time.…”