Overlapping Community Detection in Networks: the State of the Art and Comparative Study

Xie, Jierui; Kelley, Stephen; Szymanski, Boleslaw K.

doi:10.48550/arxiv.1110.5813

Cited by 7 publications

(9 citation statements)

References 36 publications

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“…Uncovering the community structure is crucial to the understanding of the structural and functional properties of realworld complex networks. As there is no universal definition of a community, many algorithms have been proposed [1], [2], [3]. They usually rely on the fact that nodes in the same community are more densely connected to each other than to the rest of the network.…”

Section: Introductionmentioning

confidence: 99%

Overlapping Community Structure in Co-authorship Networks: A Case Study

Jebabli

Cherifi

et al. 2014

2014 7th International Conference on U- And E- Service, Science and Technology

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Abstract-Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network represent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological properties of these two networks shows that they share similar topological properties. These results are very interesting. Indeed, the network of communities seems to be a good representative of the original co-authorship network. With its smaller size, it may be more practical in order to realize various analyses that cannot be performed easily in large-scale real-world networks.

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Section: Introductionmentioning

confidence: 99%

Overlapping Community Structure in Co-authorship Networks: A Case Study

Jebabli

Cherifi

et al. 2014

2014 7th International Conference on U- And E- Service, Science and Technology

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“…We have focused on k-clique percolation in this work. However, many other modern overlapping community finding algorithms exist, many of them with good scaling properties, such as link partitioning methods [20] [10], relatively scalable methods which approximate statistical objectives [21] [22], information theoretic approaches [23], methods explicitly designed to be scalable, such as label propagation [24], other clique-based methods more scalable than k-clique percolation [25] [26] and many other methods; Xie et al [27] provide a comparative analysis.…”

Section: Other Overlapping Community Finding Methodsmentioning

confidence: 99%

Percolation Computation in Complex Networks

Reid

McDaid

Hurley

2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-K-clique percolation is an overlapping community finding algorithm which extracts particular structures, comprised of overlapping cliques, from complex networks. While it is conceptually straightforward, and can be elegantly expressed using clique graphs, certain aspects of k-clique percolation are computationally challenging in practice. In this paper we investigate aspects of empirical social networks, such as the large numbers of overlapping maximal cliques contained within them, that make clique percolation, and clique graph representations, computationally expensive. We motivate a simple algorithm to conduct clique percolation, and investigate its performance compared to current best-in-class algorithms. We present improvements to this algorithm 1 , which allow us to perform k-clique percolation on much larger empirical datasets. Our approaches perform much better than existing algorithms on networks exhibiting pervasively overlapping community structure, especially for higher values of k. However, clique percolation remains a hard computational problem; current algorithms still scale worse than some other overlapping community finding algorithms.

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“…Understanding large networks and community detection in those networks is a very well researched topic [11], [12], [5]. Most of the research that have been done in this field is performed on uni-relational networks [18], [12], [14]. In one of the works by Borabora et.…”

Section: Related Workmentioning

confidence: 99%

“…Understanding evolution of communities [18], [14], [15] have gained importance over the past decade. Evolution of communities help researchers identify the evolving nature of human socialization.…”

Section: Introductionmentioning

confidence: 99%

Understanding co-evolution in large multi-relational social networks

Singhal

Roy

Srivastava

2014

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)

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Understanding dynamics of evolution in large social networks is an important problem. In this paper, we characterize evolution in large multi-relational social networks. The proliferation of online media such as Twitter, Facebook, Orkut and MMORPGs 1 have created social networking data at an unprecedented scale. Sony's Everquest 2 is one such example. We used game multi-relational networks to reveal the dynamics of evolution in a multi-relational setting by macroscopic study of the game network. Macroscopic analysis involves fragmenting the network into smaller portions for studying the dynamics within these sub-networks, referred to as 'communities'. From an evolutionary perspective of multi-relational network analysis, we have made the following contributions. Specifically, we formulated and analyzed various metrics to capture evolutionary properties of networks. We find that co-evolution rates in trust based 'communities' are approximately 60% higher than the trade based 'communities'. We also find that the trust and trade connections within the 'communities' reduce as their size increases. Finally, we study the interrelation between the dynamics of trade and trust within 'communities' and find interesting results about the precursor relationship between the trade and the trust dynamics within the 'communities'.

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Overlapping Community Detection in Networks: the State of the Art and Comparative Study

Cited by 7 publications

References 36 publications

Overlapping Community Structure in Co-authorship Networks: A Case Study

Overlapping Community Structure in Co-authorship Networks: A Case Study

Percolation Computation in Complex Networks

Understanding co-evolution in large multi-relational social networks

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