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Overlapping communities are pervasive in real-world networks. Therefore overlapping community detection is an important task for mining the structure and function of complex networks. Recently, many overlapping detection methods are proposed. Though achieving different goals, how to improve the performances of the community detection algorithms is still an open problem. In this paper, we propose a...
We propose a community detection method based on K-shell. Our method determines some core nodes of the graph according to the K-shell value of these nodes. These core nodes constitute a subgraph on which we use the community detection algorithm to divide the core nodes into communities. Compared to classical methods, by this way, our proposed method removes the non-core nodes which may impact the...
In network analysis research domain, since a lot of object and their relations are modeled as networks or graphs, network science provides a significant tool and an indispensible platform to track their complexity. Graphs exhibit a very special property: community structure. In this paper, we propose a novel community detection method in directed graphs via node similarity computation. We focus on...
Most of the existing literature which has entirely focused on clustering nodes in large-scale networks. To discover multi-scale overlapping communities quickly, we propose a highly efficient multi-resolution link community detection algorithm to detect the link communities in massive networks based on the idea of edge labeling. First, we will get the node partition of the network based on a new multi-resolution...
Community detection in complex networks has attracted a lot of attentions in recent years. Compared with the traditional single-objective community detection approaches, the multi-objective approaches based on evolutionary computation can provide a decision maker with more flexible and promising solutions. How to make effective use of the optimal solution set returned by the multi-objective community...
Community detection, as an important unsupervised learning problem in social network analysis, has attracted great interests in various research areas. Many objective functions for community detection that can capture the intuition of communities have been introduced from different research fields. Based on the classical single objective optimization framework, this paper compares a variety of these...
Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this paper, we propose a heuristic algorithm to extract the community structure in large networks based on local community attractive force optimization whose time complexity is near linear...
Structure mining plays an important part in the researches in biology, physics, Internet and telecommunications in recently emerging network science. As a main task in this area, the problem of structure mining on graph has attracted much interest and been studied in variant avenues in prior works. However, most of these works mainly rely on single chip computational capacity and have been constrained...
Community detection and tracking in social network is an important research area for many applications which are widely applied in complex systems. Recently there has been a surge of investigation in this area, fueled largely by interest in social networks, but also by interest in bibliographic citations and telecommunication records. However, due to the computational cost of the traditional algorithm...
Researches have discovered that rich interactions among entities in nature and human society bring about complex networks with community structures. In this paper, we propose a novel algorithm BiTector (bi-community detector) to mine the overlapping communities in large-scale sparse bipartite networks. We apply the algorithm to various real-world datasets, showing that BiTector can identify the overlapping...
Recently there has been considerable interest in the study of community detection in social network. However, to get more detailed knowledge about the global organization of the whole network and the discovered communities, how to explain and utilize these communities will be far more significant in many practical scenarios. Thus, we propose the problem of resume mining of social network communities...
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