Parallel community detection on large networks with propinquity dynamics

Zhang, Yuzhou; Wang, Jianyong; Wang, Yi; Zhou, Lizhu

doi:10.1145/1557019.1557127

Cited by 97 publications

(62 citation statements)

References 14 publications

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“…Zhang 15 [Zhang et al 2009] proposed an iterative process that reinforces the network topology and propinquity that is interpreted as the probability of a pair of nodes belonging to the same community. The propinquity between two vertices is defined as the sum of the number of direct links, number of common neighbors and the number of links within the common neighborhood.…”

Section: Congamentioning

confidence: 99%

Overlapping community detection in networks

2013

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This paper reviews the state of the art in overlapping community detection algorithms, quality measures, and benchmarks. A thorough comparison of different algorithms (a total of fourteen) is provided. In addition to community level evaluation, we propose a framework for evaluating algorithms' ability to detect overlapping nodes, which helps to assess over-detection and underdetection. After considering community level detection performance measured by Normalized Mutual Information, the Omega index, and node level detection performance measured by Fscore, we reached the following conclusions. For low overlapping density networks, SLPA, OSLOM, Game and COPRA offer better performance than the other tested algorithms. For networks with high overlapping density and high overlapping diversity, both SLPA and Game provide relatively stable performance. However, test results also suggest that the detection in such networks is still not yet fully resolved. A common feature observed by various algorithms in real-world networks is the relatively small fraction of overlapping nodes (typically less than 30%), each of which belongs to only 2 or 3 communities.

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Section: Congamentioning

confidence: 99%

Overlapping community detection in networks

2013

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“…A parallel clustering algorithm is suggested in [8], which is a parallelized version of DBSCAN [9]. In [10], the authors coded their community detection algorithm based on propinquity using a vertexoriented Bulk Synchronous Parallel (BSP) model to enable large scale parallelization. In [11], the authors implemented community detection algorithm on massively multithreaded Cray XMT and ran on it networks with over 100 million nodes and over a billion edges.…”

Section: Related Workmentioning

confidence: 99%

Parallel Overlapping Community Detection with SLPA

Kuzmin

Shah

Szymanski

2013

2013 International Conference on Social Computing

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Abstract-Social networks consist of various communities that host members sharing common characteristics. Often some members of one community are also members of other communities. Such shared membership of different communities leads to overlapping communities. Detecting such overlapping communities is a challenging and computationally intensive problem. In this paper, we investigate the usability of high performance computing in the area of social networks and community detection. We present highly scalable variants of a community detection algorithm called Speaker-listener Label Propagation Algorithm (SLPA). We show that despite of irregular data dependencies in the computation, parallel computing paradigms can significantly speed up the detection of overlapping communities of social networks which is computationally expensive. We show by experiments, how various parallel computing architectures can be utilized to analyze large social network data on both shared memory machines and distributed memory machines, such as IBM Blue Gene.

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“…The basic formulation of the community detection task expects as an output a partitioning of a network; that is, each node is a member of exactly one community. Numerous variants of the problem have been studied, including detection of overlapping communities where a vertex can belong to multiple communities (e.g., works by Gregory [9] and Zhang et al [22]), clustering of bipartite graphs (e.g., Papadimitriou et al [18]), and detection of clusters exploiting additional information in addition to network structure (e.g., attributes on nodes/edges, Yang et al [21]). …”

Section: Community Detectionmentioning

confidence: 99%

On community detection in real-world networks and the importance of degree assortativity

Ciglan

Laclavik

Nørvåg

2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Graph clustering, often addressed as community detection, is a prominent task in the domain of graph data mining with dozens of algorithms proposed in recent years. In this paper, we focus on several popular community detection algorithms with low computational complexity and with decent performance on the artificial benchmarks, and we study their behaviour on real-world networks. Motivated by the observation that there is a class of networks for which the community detection methods fail to deliver good community structure, we examine the assortativity coefficient of ground-truth communities and show that assortativity of a community structure can be very different from the assortativity of the original network. We then examine the possibility of exploiting the latter by weighting edges of a network with the aim to improve the community detection outputs for networks with assortative community structure. The evaluation shows that the proposed weighting can significantly improve the results of community detection methods on networks with assortative community structure.

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Parallel community detection on large networks with propinquity dynamics

Cited by 97 publications

References 14 publications

Overlapping community detection in networks

Overlapping community detection in networks

Parallel Overlapping Community Detection with SLPA

On community detection in real-world networks and the importance of degree assortativity

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