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In recent years, due to the rise of social, biological, and other rich content graphs, several novel community detection methods using structure and node attributes have been proposed. Moreover, nodes in a network are naturally characterized by multiple community memberships and there is growing interest in overlapping community detection algorithms. In this paper, we design a weighted vertex interaction model based on distance dynamics to divide the network, furthermore, we propose a distance Dynamics‐based Overlapping Semantic Community detection algorithm(DOSC) for node‐attribute networks. The method is divided into three phases: Firstly, we detect local single‐attribute subcommunities in each attribute‐induced graph based on the weighted vertex interaction model. Then, a hypergraph is constructed by using the subcommunities obtained in the previous step. Finally, the weighted vertex interaction model is used in the hypergraph to get global semantic communities. Experimental results in real‐world networks demonstrate that DOSC is a more effective semantic community detection method compared with state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

Community detection is an important methodology for understanding the intrinsic structure and function of complex networks. Because overlapping community is one of the characteristics of real-world networks and should be considered for community detection, in this article, we propose an algorithm, called link-based label propagation algorithm (LinkLPA), to detect overlapping communities. Because the link partition is conceptually natural for the problem of overlapping community detection, LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm. Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over-overlapping and incorrect partition of weak ties. Experimental results on a large number of real-world and synthetic networks show that the proposed method achieves high accuracy on detecting overlapping communities in networks. [ABSTRACT FROM AUTHOR]

Community detection is an important work for discovering the structure and features of complex networks. Many existing methods are sensitive to critical user-dependent parameters or time-consuming in practice. In this paper, we propose a novel label propagation algorithm, called CenLP (Centrality-based Label Propagation). The algorithm introduces a new function to measure the centrality of nodes quantitatively without any user interaction by calculating the local density and the similarity with higher density neighbors for each node. Based on the centrality of nodes, we present a new label propagation algorithm with specific update order and node preference to uncover communities in large-scale networks automatically without imposing any prior restriction. Experiments on both real-world and synthetic networks manifest our algorithm retains the simplicity, effectiveness, and scalability of the original label propagation algorithm and becomes more robust and accurate. Extensive experiments demonstrate the superior performance of our algorithm over the baseline methods. Moreover, our detailed experimental evaluation on real-world networks indicates that our algorithm can effectively measure the centrality of nodes in social networks. [ABSTRACT FROM AUTHOR]