An Algorithm to Find Overlapping Community Structure in Networks

Gregory, Steven

doi:10.1007/978-3-540-74976-9_12

Cited by 230 publications

(176 citation statements)

References 15 publications

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“…Similarly, the heuristic rule employed by the GN algorithm [2] is that the ‚edge betweenness‛ of inter-community links should be larger than that of intra-community links. Others such as the Wu-Huberman algorithm [28], the HITS algorithm [13], the CPM [29], and the FEC [30] have adopted different assumptions.…”

Section: Literature Reviewmentioning

confidence: 99%

Community Detection in Dynamic Social Networks: A Survey

Enugala¹,

Rajamani²,

Ali³

et al. 2015

IJRA

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Social network analysis has gained much attention these days. These networks can be represented as a graph. In this graph each individual is represented as a node and the relationship between them is represented as an edge. Community detection in social networks plays a vital role. A community in social networks indicates that nodes within the group are densely connected and the connections between groups are sparse. As the activities and interaction between the entities change over time, the speed with which the network is changing is phenomenal. Because of this frequent change it has become very important to detect the community in dynamic social networks. Many methods have been proposed for the community detection in dynamic social networks. The community detection in dynamic social networks helps to understand the network structure and analyze the network properties. In this paper, various community detection methods have been studied and a comparison between them is presented.

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Section: Literature Reviewmentioning

confidence: 99%

Community Detection in Dynamic Social Networks: A Survey

Enugala¹,

Rajamani²,

Ali³

et al. 2015

IJRA

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show abstract

“…Recently, Palla et al [5] propose the CFinder system to partition complex networks to k-clique communities, where k is a given parameter as clique size. Gregory proposes the CONGA algorithm [1] based on the betweenness score [3] and later extends it to the CONGO algorithm to improve the scalability [6]. He also shows that CONGO provides the same level of performance as CFinder, on synthetic networks.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the fact that they intentionally focus on overlapping community structure makes them find or force overlap even for data without such structure. More importantly, many approaches not only require parameters that are difficult to determine but also their results are very sensitive to parameter settings, e.g., number of communities [1,20], community density [5,18], or size of a local community region [6].…”

Section: Related Workmentioning

confidence: 99%

“…A community (or cluster ) can be seen as a subgraph such that the density of edges within the subgraph is greater than the density of edges between its nodes and nodes outside it [1]. Existing community detection approaches, such as spectral clustering [2], modularity-based [3] and density-based methods [4] achieve good results for some datasets, and have proposed various metrics to measure the similarity between social entities.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the definition would be different across various domains, or even across different networks of the same domain. Therefore, most proposed approaches [1,6,7,5,4] for overlapping community detection require the user to describe the communities they are looking for by giving parameters, e.g., community size, density range, the number of communities, etc. However, appropriate parameters are usually extremely hard to determine without tedious and repeated testing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ONDOCS: Ordering Nodes to Detect Overlapping Community Structure

Chen

Zaı̈ane

Sander

et al. 2010

Data Mining for Social Network Data

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Finding communities is an important task for the discovery of underlying structures in social networks. While existing approaches give interesting results, they typically neglect the fact that communities may overlap, with some hub nodes participating in multiple communities. Similarly, most methods cannot deal with outliers, which are nodes that belong to no germane communities. The definition of community is still vague and the criterion to locate hubs or outliers vary. Existing approaches usually require guidance in this regard, specified as input parameters, e.g., the number of communities in the network, without much intuition. Here we present a general community definition and a list of requirements for a community mining metric. We review advantages and disadvantages of existing metrics and propose our new metric to quantify the relation between nodes in a social network. We then use the new metric to build a visual data mining system, which first helps the user to achieve appropriate parameter selection by observing initial data visualizations, then detects overlapping community structure from the network while also excluding outliers. Experiment results verify the scalability and accuracy of our approach on real data networks and show its advantages over existing methods that also consider overlaps. An empirical evaluation of our metric demonstrates superior performance over previous measures.

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Extracting evolutionary communities in community question answering

Zhang

Zeng

et al. 2014

Asso for Info Science & Tech

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With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.

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An Algorithm to Find Overlapping Community Structure in Networks

Cited by 230 publications

References 15 publications

Community Detection in Dynamic Social Networks: A Survey

Community Detection in Dynamic Social Networks: A Survey

ONDOCS: Ordering Nodes to Detect Overlapping Community Structure

Extracting evolutionary communities in community question answering

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