An Improved Local Community Detection Algorithm Using Selection Probability

In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods

Overlapping Community Detection Extended from Disjoint Community Structure

Community detection is a hot issue in the study of complex networks. Many community detection algorithms have been put forward in different fields. But most of the existing community detection algorithms are used to find disjoint community structure. In order to make full use of the disjoint community detection algorithms to adapt to the new demand of overlapping community detection, this paper proposes an overlapping community detection algorithm extended from disjoint community structure by selecting overlapping nodes (ONS-OCD). In the algorithm, disjoint community structure with high qualities is firstly taken as input, then, potential members of each community are identified. Overlapping nodes are determined according to the node contribution to the community. Finally, adding overlapping nodes to all communities they belong to and get the final overlapping community structure. ONS-OCD algorithm reduces the computation of judging overlapping nodes by narrowing the scope of the potential member nodes of each community. Experimental results both on synthetic and real networks show that the community detection quality of ONS-OCD algorithm is better than several other representative overlapping community detection algorithms

A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods

An Autonomous Divisive Algorithm for Community Detection Based on Weak Link and Link-Break Strategy

Divisive algorithms are widely used for community detection. A common strategy of divisive algorithms is to remove the external links which connect different communities so that communities get disconnected from each other. Divisive algorithms have been investigated for several decades but some challenges remain unsolved: (1) how to efficiently identify external links, (2) how to efficiently remove external links, and (3) how to end a divisive algorithm with no help of predefined parameters or community definitions. To overcome these challenges, we introduced a concept of the weak link and autonomous division. The implementation of the proposed divisive algorithm adopts a new link-break strategy similar to a tug-of-war contest, where communities act as contestants and weak links act as breakable ropes. Empirical evaluations on artificial and real-world networks show that the proposed algorithm achieves a better accuracy-efficiency trade-off than some of the latest divisive algorithms