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Xu, Xiaowei; Yuruk, Nurcan; Feng, Zhidan; Schweiger, Thomas A. J.

doi:10.1145/1281192.1281280

Cited by 618 publications

(38 citation statements)

References 15 publications

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“…Hence, community detection is referred often as graph clustering problem. Much prior works has been done on clustering as well as community detection in several domains (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008;Boykov & Funka-Lea, 2006;Grady, 2006;He & Chen, 2015;Jeevan et al, 2011;Kanawati, 2011;Li & Wu, 2015;Liu & juan Ban, 2015;Lu, Sun, Wen, Cao, & La Porta, 2015;Shah & Zaman, 2010;Xu, Yuruk, Feng, & Schweiger, 2007;Wei et al, 2015). During community exploration process, properties of network elements are generally analyzed in three levels of abstraction.…”

Section: Related Workmentioning

confidence: 99%

“…Initially, individual nodes are considered as communities, aggregate these communities to get next level communities and so on until attains optimal modularity. Besides detecting communities SCAN (Xu et al, 2007) also detects hubs and outliers with respect to communities. Leader based agglomerative approaches such as Leader-Follower (LeadF) (Shah & Zaman, 2010) and LICOD (Kanawati, 2011) draws attention in recent years.…”

Section: Related Workmentioning

confidence: 99%

“…We have considered six state-of-the-art algorithms FastU (Blondel et al, 2008) (Xu et al, 2007) to perform comparative analysis with our proposed approach ENBC. Among these six algorithms HC-PIN, LICOD and SCAN have specific parameters to control community structure.…”

Section: Algorithm Parameter Settingmentioning

confidence: 99%

See 2 more Smart Citations

Investigating community structure in perspective of ego network

Biswas

2015

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Investigating community structure in perspective of ego network

Biswas

2015

Expert Systems with Applications

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“…It includes clustering based on normalized cut [22], max flow min-cut problem [3,26], structural density and modularity [13,19]. These approaches partition the set of vertices in different groups and estimate the edge cut cost, i.e.…”

Section: Related Workmentioning

confidence: 99%

Intra graph clustering using collaborative similarity measure

Nawaz

Khan

Lee

et al. 2015

Distrib Parallel Databases

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Graph is an extremely versatile data structure in terms of its expressiveness and flexibility to model a range of real life phenomenon. Various networks like social networks, sensor networks and computer networks are represented and stored in the form of graphs. The analysis of these kind of graphs has an immense importance from quite a long time. It is performed from various aspects to get maximum out of such multifaceted information repository. When the analysis is targeted towards finding groups of vertices based on their similarity in a graph, clustering is the most conspicuous option. Previous graph clustering approaches either focus on the topological structure or attributes likeness, however, few recent methods constitutes both aspects simultaneously. Due to enormous computation requirements for similarity estimation, these methods are often suffered from scalability issues. In order to overcome this limitation, we introduce collaborative similarity measure (CSM) for intra-graph clustering. CSM is based on shortest path strategy, instead of all paths, to define structural and semantic relevance among vertices. First, we calculate the pair-wise similarity among vertices using CSM. Second, vertices are grouped together based on calculated similarity under k-Medoid framework. Empirical analysis, based on density, and entropy, proves the efficacy of CSM over existing measures. Moreover, CSM becomes 123 Distrib Parallel Databases a potential candidate for medium scaled graph analysis due to an order of magnitude less computations.

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“…Recently, the characterization of community (or module) structures [4] in complex networks has been paid a considerable amount of attention by the scientific community [4][5][6][7][8][9]. A community in a network is usually thought of as a group of nodes that are similar to each other and dissimilar from the rest of the network.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks

Wang

Yang

et al. 2009

2009 Fourth International Conference on Computer Sciences and Convergence Information Technology

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Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual crossover operator based on ensemble learning. Therefore, GAEL can avoid the problems that are brought by traditional crossover operator which is only able to mix string blocks of different individuals, but not able to recombine clustering contexts of different individuals into new better ones. In addition, the local search strategy, which makes mutated node be placed into the community where most of its neighbors are, is used in mutation operator. At last, a Markov random walk based method is used to initialize population in this paper, and it can provide us a population of accurate and diverse clustering solutions. Those diverse and accurate individuals are suitable for ensemble learning based multi-individual crossover operator. The proposed GAEL is tested on both computer-generated and real-world networks, and compared with current representative algorithms for community detection in complex networks. Experimental results demonstrate that GAEL is highly effective at discovering community structure.

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Cited by 618 publications

References 15 publications

Investigating community structure in perspective of ego network

Investigating community structure in perspective of ego network

Intra graph clustering using collaborative similarity measure

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks

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