Tracking the evolution of overlapping communities in dynamic social networks

Detecting communities in complex networks has been one of the most popular research areas in recent years. There have been many community detection algorithms proposed to date. However, the local information (cliques) of communities and the search efficiency of algorithm have not been considered both in previous studies. In this paper, we propose a novel local expansion algorithm for detecting overlapping communities based on cliques. The algorithm draws on the assumption that cliques are the core of communities, as the clique takes into account the local characteristics of the community. The proposed algorithm adopts a single node with the maximum density as an initial community to prevent the formation of a large number of near-duplicate community structures, which improves the search efficiency of the algorithm. In many experiments using computer-generated and real-world networks, the proposed algorithm based on this idea verifies that the algorithm is able to detect overlapping communities effectively. The experiment yields better community uncover results, and the time efficiency and the complexity of algorithm are also satisfactory.

Section: Related Workmentioning

confidence: 99%

Local Optimization for Clique-Based Overlapping Community Detection in Complex Networks

Fan

2020

“…Recent studies have shown that community and community structure in social networks share some distinctive characteristics. For example, Wang et al [21] believed that communities in social networks are not only overlapping but also evolving, so community evolution must be tracked. Xu et al [22] quantified the changes in dynamic communities and studied a method to detect dynamic communities and identify key evolutionary events.…”

Section: A Community Community Structure and Community Detectionmentioning

confidence: 99%

“…For preferably detecting communities in networks, scholars in different disciplines have introduced their own tools into this field, including extremum optimization [25], spectrum optimization [14], simulated annealing [26], penetration [20], and local extension [16], [17], [21]. Probabilistic models, e.g., random walk [19], Markov random field [27], and stochastic blockmodels [28], have also been widely studied.…”

Section: A Community Community Structure and Community Detectionmentioning

confidence: 99%

Divisive Algorithm Based on Node Clustering Coefficient for Community Detection

Jin

2020

This paper studies the relationship between the clustering coefficient of nodes and the community structure of the network. Communities in a network are regarded as node-induced subgraphs of the network in this study. We define the border of a subgraph and the node network density of a node and present a formal definition of community from the view of examining the subgraph borders. Afterward, we analyze the relationship between the change in node clustering coefficients and in node network density and set the rule for identifying intercommunity edges. Finally, we propose a novel divisive algorithm for community detection by iteratively removing intercommunity edges. The time complexity of our algorithm is O(N d 2), which increases linearly with the network size. Experiments on both synthetic and real-world networks show that introducing node clustering coefficients into the divisive algorithm can greatly improve the time efficiency of the algorithm while guaranteeing the accuracy of community detection.

“…Many researchers have proposed numerous improved algorithms. For example, the DOCET algorithm [24] is analysed under the topological potential field in the valley structure according to the node position. However, through the experimental process, it is proved that, for the DOCET algorithm, although the value of modularity is large, the number of community partitions is also large.Partitioning the community according to the theory of topological potential causes three or four nodes to be isolated as a community.There are a large number of isolated communities that are easy to affect the public opinion push and community expansion of the real scene.HCDTP algorithm [25] divides the initial community according to the node topology potential, and selects the community corresponding to the maximum module degree as the final community structure by community merging.…”

Section: Introductionmentioning

confidence: 99%

The Multi-Dimensional Information Fusion Community Discovery Based on Topological Potential

Fei

et al. 2020

Many community discovery algorithms add attribute information of nodes to further improve the quality of community division in the complex network with redundant and discrete data, but these algorithms lack of multi-dimensional information, such as users' interests in social networks, social relations, geography and education background, in addition to topological structure and attribute information. Therefore,this paper proposes a Multi-dimensional Information Fusion Community Discovery(MIFCD) method. Firstly, based on the idea of label propagation, link information and attribute information are combined to get link weights between nodes. Secondly, link weights are added to the topology potential to divide the sub group communities. Finally, the sub group communities are combined by using the distance information and attribute information of the core nodes between communities. In order to verify the effectiveness of the algorithm proposed in this paper, the algorithm is compared with six community partition algorithms which only consider the link information of nodes and consider the two kinds of information of node attributes and links. Experiment results on eight social networks show that this method can effectively improve the quality of community classification in both attribute communities and non-attribute communities by analyzing four evaluation indexes: improved modular degree, information entropy, community overlap degree and comprehensive index. INDEX TERMS Community division, complex network, discrete data, multi-dimensional information.