As the community detection is able to facilitate the discovery of hidden information in complex networks, it has been drawn a lot of attention recently. However, due to the growth in computational power and data storage, the scale of these complex networks has grown dramatically. In order to detect communities by utilizing global approaches, it is required to have all the global information of the whole network; something which is impossible, because of the rapid growth in the size of the networks. In this paper, a local approach has been proposed based on the detection and expansion of core nodes. First, a community’s central node (core node) which has a high level of embeddedness is detected based on the similarity between graph’s nodes. By using this, the total weights of a weighted graph’s edges created. Following by that, the expansion of these nodes will be considered, by utilizing the concept of node’s membership based on the definition of strong community for weighted graphs. It can be seen that in detecting communities, the more accurate the weights of edges detected based on the node similarity, the more precise the local algorithm will be. In fact, the algorithm has the ability to detect all the graph’s communities in a network using local information as well as identifying various roles of nodes, either being (core or outlier). Test results on both real-world and artificial networks prove that the quality of the communities which are detected by the proposed algorithm is better than the results which are achieved by other state-of-the-art algorithms in the complex networks.

Community detection in large-scale networks is one of the main challenges in social networks analysis. Proposing a fast and accurate algorithm with low time complexity is vital for large-scale networks. In this paper, a fast community detection algorithm based on local balanced label diffusion (LBLD) is proposed. The LBLD algorithm starts with assigning node importance score to each node using a new local similarity measure. After that, top 5% important nodes are selected as initial rough cores to expand communities. In the first step, two neighbor nodes with highest similarity than others receive a same label. In the second step, based on the selected rough cores, the proposed algorithm diffuses labels in a balanced approach from both core and border nodes to expand communities. Next, a label selection step is performed to ensure that each node is surrendered by the most appropriate label. Finally, by utilizing a fast merge step, final communities are discovered. Besides, the proposed method not only has a fast convergence speed, but also provides stable and accurate results. Moreover, there is no randomness as well as adjustable parameter in the LBLD algorithm. Performed experiments on real-world and synthetic networks show the superiority of the LBLD method compared with examined algorithms.