Local Community Detection Using Seeds Expansion

Xu, Bingying; Liang, Zheng; Jia, Yan; Zhou, Bin; Han, Yi

doi:10.1109/cgc.2012.69

Cited by 3 publications

(7 citation statements)

References 24 publications

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“…Node(s) Bagrow J. et al [30],Xu B. et al [12],Zhang T. et al [31],Chen Q. Et al [32],Moradi F. et al [33],Xia S. et al [10],Fanrong M. et al [34],Wang P. et al [35],Hamann M. et al [36],Ding X. et al [37],Tasgin M. et al [38],Xu X.…”

Section: Focus On Startingmentioning

confidence: 99%

“…This problem is also known as seed set expansion [11]. The practical importance of LCD is very significant for numerous real-world applications, such as analysing terrorist activities or determining the placement of advertisements in social media [12]. Furthermore, LCD VOLUME 4, 2016 can be applied in web search, where with a few known pages containing similar information, one can generate a larger set of web pages that contain the relevant content related to a particular search query.…”

Section: Introductionmentioning

confidence: 99%

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Local Community Detection: A Survey

2022

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Community detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local community detection is important for numerous real-world applications such as protein interactions and targeted advertising. Since 2005, when the first research paper on local community detection appeared, the literature has been vast and difficult to navigate, as each method works best under certain conditions and assumptions regarding the seed nodes and the identification of their community. For this reason, and motivated by the many real-world applications of local community detection, in this paper we provide a comprehensive overview and taxonomy of local community detection algorithms. There are quite a lot of surveys on community detection that make a compendious reference to local community detection. However, they do not achieve a systematic and comprehensive coverage of this particular field. Since the research area of local community detection is quite extensive, it is necessary to categorize and discuss the various methods, techniques, and assumptions used to address the problem. This survey aims to fill this gap and help researchers get a clear overview of the local community detection problem. To this end, we have also gathered the best documented tools and the most commonly used datasets in the local community detection literature to help researchers identify the tools they can use to prove their methods.

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Section: Focus On Startingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Community Detection: A Survey

2022

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“…As an alternative to community expansion we propose our adaptation of SCAN (Structural Clustering Algorithm for Networks) [29], a global community detection algorithm inspired by the data-clustering algorithm DBSCAN [12]. SCAN tries to transfer the characteristics of DBSCAN to community detection in networks.…”

Section: B Selscan: a Density-based Approachmentioning

confidence: 99%

Detecting communities around seed nodes in complex networks

Staudt

Marrakchi

Meyerhenke

2014

2014 IEEE International Conference on Big Data (Big Data)

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The detection of communities (internally dense subgraphs) is a network analysis task with manifold applications. The special task of selective community detection is concerned with finding high-quality communities locally around seed nodes. Given the lack of conclusive experimental studies, we perform a systematic comparison of different previously published as well as novel methods. In particular we evaluate their performance on large complex networks, such as social networks. Algorithms are compared with respect to accuracy in detecting ground truth communities, community quality measures, size of communities and running time. We implement a generic greedy algorithm which subsumes several previous efforts in the field. Experimental evaluation of multiple objective functions and optimizations shows that the frequently proposed greedy approach is not adequate for large datasets. As a more scalable alternative, we propose selSCAN, our adaptation of a global, density-based community detection algorithm. In a novel combination with algebraic distances on graphs, query times can be strongly reduced through preprocessing. However, selSCAN is very sensitive to the choice of numeric parameters, limiting its practicality. The random-walk-based PageRankNibble emerges from the comparison as the most successful candidate.

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“…This is of the same order as that in Clauset (2005) (when k 1,max is a constant). Unlike the agglomerative approach in Clauset (2005); Xu et al (2012); Huang et al (2013) that only works for a specific objective function, our local community detection algorithm has the freedom to choose the "viewpoint" to sample a graph. In particular, we consider sampling a graph with a random walk that has a path length not greater than 2.…”

Section: Introductionmentioning

confidence: 98%

“…As such, local community detection cannot be done locally. Another approach is the agglomerative approach (Clauset, 2005;Xu et al, 2012;Huang et al, 2013) that repeatedly adds a "relatively important" node to the community until the maximum size of the community is reached. An agglomerative approach for local community detection is more computationally efficient as such an approach only needs to explore the network locally.…”

Section: Introductionmentioning

confidence: 99%

Relative centrality and local community detection

Chang¹,

Chang²,

Hsieh³

et al. 2015

Net Sci

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In this paper, we develop a formal framework for what a good community should look like and how strong a community is (community strength). One of the key innovations is to incorporate the concept of relative centrality into structural analysis of networks. In our framework, relative centrality is a measure that measures how important a set of nodes in a network is with respect to another set of nodes, and it is a generalization of centrality. Building on top of relative centrality, the community strength for a set of nodes is measured by the difference between its relative centrality with respect to itself and its centrality. A community is then a set of nodes with a nonnegative community strength. We show that our community strength is related to conductance that is commonly used for measuring the strength of a small community. We define the modularity for a partition of a network as the average community strength for a randomly selected node. Such a definition generalizes the original Newman's modularity and recovers the stability in as special cases. For the local community detection problem, we also develop efficient agglomerative algorithms that guarantee the community strength of the detected local community.

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Local Community Detection Using Seeds Expansion

Cited by 3 publications

References 24 publications

Local Community Detection: A Survey

Local Community Detection: A Survey

Detecting communities around seed nodes in complex networks

Relative centrality and local community detection

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