Community detection in large-scale social networks

Du, Nan; Wu, Bin; Pei, Xin; Bai, Wei; Xu, Liutong

doi:10.1145/1348549.1348552

Cited by 176 publications

(99 citation statements)

References 18 publications

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“…Community detection is also a common research problem on other real-life social networks, such as scientific collaboration networks [17], [18]. However, these methods consider only topological links to detect community structures, which does not translate to interactive communities [4], [5].…”

Section: Background and Related Workmentioning

confidence: 99%

Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

Lim¹,

Datta²

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hashtags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users.

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Section: Background and Related Workmentioning

confidence: 99%

Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

Lim¹,

Datta²

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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“…Jain [16] gives a thorough presentation of many clustering methods and classifies them into partitioning [21,20], hierarchical [13,15,29], densitybased [3,30], grid-based [2,27] and model-based methods [9,19]. New graph-based methods have also been developed in the emerging field of community detection [10,25,26]. Fortunato [11] covers many of the latest ones.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Approach Using Weighted Similarity Majority Margins

Bisdorff

Meyer

Olteanu

2011

Advanced Data Mining and Applications

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Abstract. We propose a meta-heuristic algorithm for clustering objects that are described on multiple incommensurable attributes defined on different scale types. We make use of a bipolar-valued dual similaritydissimilarity relation and perform the clustering process by first finding a set of cluster cores and then building a final partition by adding the objects left out to a core in a way which best fits the initial bipolar-valued similarity relation.

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“…Social network analysis, where the data of interest are people and the relationships among them, is another very natural setting for graph processing. Significant work has been done on the detection of communities [4,5] and influential figures [6] in social networks, frequently using a graph as the primary data structure.…”

Section: Introductionmentioning

confidence: 99%

Toward signal processing theory for graphs and non-Euclidean data

Miller

Bliss

Wolfe

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example: detection of a subgraph embedded in a background graph. We describe an approach based on detection theory and provide empirical results indicating that the test statistic proposed has reasonable power to detect dense subgraphs in large random graphs.

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Community detection in large-scale social networks

Cited by 176 publications

References 18 publications

Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

A Clustering Approach Using Weighted Similarity Majority Margins

Toward signal processing theory for graphs and non-Euclidean data

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