LinkSCAN*: Overlapping community detection using the link-space transformation

Lim, Sungsu; Ryu, Seungwoo; Kwon, Sejeong; Jung, Kyomin; Lee, Jae-Gil

doi:10.1109/icde.2014.6816659

Cited by 74 publications

(47 citation statements)

References 20 publications

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“…Clique percolation was successfully used on real-world networks [15,16]. Besides structure based method like CPM, overlapping community detection could also be modeled as link-partition problem [2,10,18]. It first converts the original graph G into link graph L(G), in which each node is a link of L(G), and two nodes are adjacent if the two links they represent have common node in G. Then link partition of G can be mapped to node partition of L(G), and by performing random walk [10], Jaccard-type similarity computation [2], or density-based clustering algorithm SCAN [18], node clusters of L(G) are derived and then they can be converted to overlapping node communities of G. Label propagation method has been also widely used for detecting communities [14,26], they propagate all nodes' labels to their neighbors for one step to make their community membership reach a consensus.…”

Section: Related Workmentioning

confidence: 99%

“…This phenomenon could be easily explained in social media: individuals could belong to numerous communities related to their social activities, hobbies, friends and so on. Thus, overlapping community detection (OCD) [10,14,18,20] has drawn a lot of attention in recent years. OCD dedicates to find all overlapping communities of the entire network, which has shortcomings in some applications: First, it is time consuming when the network is quite large.…”

Section: Introductionmentioning

confidence: 99%

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Searching overlapping communities for group query

et al. 2015

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In most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. Thus, overlapping community discovery has drawn a great deal of attention and there is a lot of research on it. However, most work has focused on community detection, which takes the whole network as input and derives all communities at one time. Community detection can only be used in offline analysis of networks and it is quite costly, not flexible and can not support dynamically evolving networks. Online community search which only finds overlapping communities containing a given node is a flexible and light-weight solution, and also supports dynamic graphs very well. However, in some scenarios, it requires overlapping community search for group query, which means that the input is a set of nodes instead of one single node. To solve this problem, we propose an overlapping community search framework for group query, including both exact and heuristic solutions. The heuristic solution has four strategies, some of which are adjustable and self-adaptive. We propose two parameters node degree and discovery power to trade off the efficiency and quality of the heuristic strategies, in order to make This paper is an extended version of our previous conference paper [24]. World Wide Web them satisfy different application requirements. Comprehensive experiments are conducted and demonstrate the efficiency and quality of both exact and heuristic solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Searching overlapping communities for group query

et al. 2015

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“…In 2014, Lim etc. introduced DBScan clustering algorithm into link community detection and proposed Link Scan algorithm [8].…”

Section: Introductionmentioning

confidence: 99%

Link Similarity Reveals Multiscale Link Communities

Wang

Huang

2015

Advances in Intelligent Systems and Computing

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Overlapping community detection has been a hot topic in the research of complex network. In this paper, we proposed a novel link clustering method (NLC) for overlapping community detection. The method is consisted of two main steps. First step is a link similarity. The link similarity is to use a link similarity with a property of convergence to consider relationship of undirected links. The second step combines Markov Clustering Method with link similarity matrix got by first step with an extended measure of quality of modularity to determine the best partition of link communities. Extensive experiments on real world networks show our method is more reliable and reasonable than the other compared algorithms. Through varying parameters of our link similarity, our NLC method reveals multiscale link communities.

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“…32 Community detection is to partition the set of network vertices into multiple groups such that the vertices within a group are connect-33 ed densely, but connections between groups are sparse [4]. There have been many studies regarding community detection [5]. The…”

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confidence: 99%