Efficient Identification of Overlapping Communities

Baumes, Jeffrey; Goldberg, Mark; Magdon‐Ismail, Malik

doi:10.1007/11427995_3

Cited by 135 publications

(127 citation statements)

References 6 publications

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“…In other words, each clusters have unique members, whereas in reality, people and objects can belong to multiple groups [15]. Recently, many researchers have proposed detecting uncovering algorithms such as [16]- [19], but they are often requiring large computation time and constraints.…”

Section: Community Detectionmentioning

confidence: 99%

Estimating Social Tie Strength for Autonomous D2D Collaborations

Choi¹,

Kang²,

Jung³

et al. 2015

IJFCC

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7 Abstract-With the new paradigm, Internet of Things, devices are now able to share services and resources generating higher level of services. However, the management and configuration of the smart devices are often troublesome, interrupting user's primary task. To automate the process of device collaboration, smart devices should be able to configure and manage themselves, self-identifying peer devices and their relationships. Hence, we proposed the concept of device sociality, which describes social relationship between devices. In device social network with device sociality, devices are able to detect peer devices and determine resources and services to share. To derive device sociality, we investigated social relationships between users and analyzed correlations between online social interactions and social relationships. Moreover, we also investigated social ties in terms of interaction direction, individual peculiarity, and network topology. In the experiment, we not only derived directional and individualized social affinity models, but also detected organizational structure and groups of the participants, confirming the potential of D2D collaboration.Index Terms-Device social, social relationship, social network, internet of things, communication, relationship model, tie strength.

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Section: Community Detectionmentioning

confidence: 99%

Estimating Social Tie Strength for Autonomous D2D Collaborations

Choi¹,

Kang²,

Jung³

et al. 2015

IJFCC

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“…In order to determine groups in the data, the Iterative Scan algorithm presented in [2] was used. This algorithm produces sets of vertices which are locally optimal with respect to some density function.…”

Section: Community and Evolution Statisticsmentioning

confidence: 99%

“…Without this term, sparse areas of the graph can be added to a cluster quite easily resulting in very large communities with high diameters. The algorithm was seeded using the Link Aggregate algorithm described in [2]. The number of clusters produced after optimization via Iterative Scan, their average size, average density, and average edge probability are all shown in Figure 8.…”

Section: Community and Evolution Statisticsmentioning

confidence: 99%

Stable Statistics of the Blogograph

Goldberg

Magdon‐Ismail

Kelley

et al. 2009

Lecture Notes in Computer Science

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Abstract. The primary focus of this paper is to describe stable statistics of the blogosphere's evolution which convey information on the social network's dynamics. In this paper, we present a number of non-trivial statistics that are surprisingly stable and thus can be used as benchmarks to diagnose phase-transitions in the network. We believe that stable statistics can be used to identify anomalous behavior at all levels: that of a node, of a local community, or of the entire network itself. Any substantial change in those stable statistics must alert the researchers and analysts to the need for further investigation. Furthermore, the usage of these or similar statistics that are based solely on the communication dynamics and not on the communication content, allows one to diagnose anomalous behavior with minimal intrusion of privacy.

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“…Along with the rapid development of network clustering techniques, the ability of revealing overlaps between communities has become very important as well [86,9,39,83,31,89,57,71,52]. Indeed, communities in realworld graphs are often inherently overlapping: each person in a social web belongs usually to several groups (family, colleagues, friends, etc.…”

Section: Applications: Community Finding and Clusteringmentioning

confidence: 99%

k-Clique Percolation and Clustering

Palla

Ábel

Farkas

et al. 2008

Bolyai Society Mathematical Studies

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We summarise recent results connected to the concept of k-clique percolation. This approach can be considered as a generalisation of edge percolation with a great potential as a community finding method in real-world graphs. We present a detailed study of the critical point for the appearance of a giant kclique percolation cluster in the Erdős-Rényi-graph. The observed transition is continuous and at the transition point the scaling of the giant component with the number of vertices is highly non-trivial. The concept is extended to weighted and directed graphs as well. Finally, we demonstrate the effectiveness of k-clique percolation as a community finding method via a series of real-world applications.

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Efficient Identification of Overlapping Communities

Cited by 135 publications

References 6 publications

Estimating Social Tie Strength for Autonomous D2D Collaborations

Estimating Social Tie Strength for Autonomous D2D Collaborations

Stable Statistics of the Blogograph

k-Clique Percolation and Clustering

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