Spatial Scan Statistics for Graph Clustering

Wang, Bei; Phillips, Jeff M.; Schreiber, Robert; Wilkinson, Dennis M.

doi:10.1137/1.9781611972788.66

Cited by 34 publications

(28 citation statements)

References 39 publications

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“…However, there are few studies and methods applying this approach to detect clusters in social networks. Wang et al (2008) first used a scan statistic to detect clusters in a social network. We briefly introduce the basic idea of the scanning method and extend it to networks with PL-distributed attributes.…”

Section: Continuous Power-law Distributionmentioning

confidence: 99%

“…To provide a statistical significance of cluster detection without considering the priors, Wang et al (2008) provided a scanning method for testing clusters based on the idea of cluster detection in the spatial data analysis (Kulldorff 1997). Under the assumption of Poisson random model, Wang et al (2008) constructed a scan statistic for detecting structure clusters in social networks.…”

Section: Introductionmentioning

confidence: 99%

“…Under the assumption of Poisson random model, Wang et al (2008) constructed a scan statistic for detecting structure clusters in social networks. Instead of considering the structure clusters, We intend to generalize scan statistics to consider the attribute of networks with PL distributions.…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to extend the use of the scanning method provided in Wang et al (2008) to the network whose attributes are PL distributed. In Sects.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Power-law distributions of attributes in community detection

Wang

Phoa

Hsu

2015

Soc. Netw. Anal. Min.

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Community detection has drawn significant attention as new media generates big data every day. To provide statistical testing procedures for community detection in social networks, a scanning method has been developed based on the likelihood of Poisson random graph. However, the scan statistics did not consider detecting communities of the attributes with power-law distribution. Power-law distribution, generally followed by network attributes, is conspicuous in many scientific situations. This paper aims at extending the scanning method to analyze a social network in which attributes follow power-law distribution. Besides the theoretical construction, simulation studies are performed to verify the feasibility of the proposed method, and an authorship network is used to demonstrate the proposed method.

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Section: Continuous Power-law Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This study aims to extend the use of the scanning method provided in Wang et al (2008) to the network whose attributes are PL distributed. In Sects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Power-law distributions of attributes in community detection

Wang

Phoa

Hsu

2015

Soc. Netw. Anal. Min.

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show abstract

“…An alternative approach to accomplish this, which we have yet to examine, is given in [33]. After determining and queuing for each node v ∈ V its dense region, regions are popped from the queue and contracted.…”

Section: Introductionmentioning

confidence: 99%

Orca Reduction and ContrAction Graph Clustering

Delling

Görke

Schulz

et al. 2009

Algorithmic Aspects in Information and Management

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Abstract. During the last years, a wide range of huge networks has been made available to researchers. The discovery of natural groups, a task called graph clustering, in such datasets is a challenge arising in many applications such as the analysis of neural, social, and communication networks. We here present Orca, a new graph clustering algorithm, which operates locally and hierarchically contracts the input. In contrast to most existing graph clustering algorithms, which operate globally, Orca is able to cluster inputs with hundreds of millions of edges in less than 2.5 hours, identifying clusterings with measurably high quality. Our approach explicitly avoids maximizing any single index value such as modularity, but instead relies on simple and sound structural operations. We present and discuss the Orca algorithm and evaluate its performance with respect to both clustering quality and running time, compared to other graph clustering algorithms.

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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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Spatial Scan Statistics for Graph Clustering

Cited by 34 publications

References 39 publications

Power-law distributions of attributes in community detection

Power-law distributions of attributes in community detection

Orca Reduction and ContrAction Graph Clustering

Extracting evolutionary communities in community question answering

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