Large scale cohesive subgraphs discovery for social network visual analysis

Zhao, Feng; Tung, Anthony K. H.

doi:10.14778/2535568.2448942

Cited by 98 publications

(47 citation statements)

References 24 publications

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“…Wang and Cheng proposed an out-of-memory algorithm for truss decomposition and a top-t k-truss evaluation algorithm [2]. Recently, Zhao and Tung [30] studied the truss decomposition problem and consider that the networked data is stored in a graph database. They also studied how to visualize the graph.…”

Section: Related Workmentioning

confidence: 99%

Discovering Hierarchical Subgraphs of K-Core-Truss

Zhang

et al. 2018

Data Sci. Eng.

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Discovering dense subgraphs in a graph is a fundamental graph mining task, which has a wide range of applications in social networks, biology and visualization to name a few. Even the problem of computing most cohesive subgraphs is NPhard (like clique, quasi-clique, k-densest subgraph), there exists a polynomial time algorithm for computing the k-core and k-truss. In this paper, we propose a novel dense subgraph model, k-core-truss, which leverages on a new type of important edges based on the basis of k-core and k-truss. We investigate the structural properties of the k-core-truss model. Compared to k-core and k-truss, k-core-truss can significantly discover the interesting and important structural information out the scope of k-core and k-truss. We study two useful problems of k-core-truss decomposition and k-core-truss search. In particular, we develop a k-core-truss decomposition algorithm to find all k-core-truss in a graph G by iteratively removing edges with the smallest degree-support. In addition, we offer a k-core-truss search algorithm to identifying a particular k-core-truss containing a given query node such that the core number k is the largest. Extensive experiments on several web-scale real-world datasets show the effectiveness and efficiency of k-core-truss model and proposed algorithms.

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Section: Related Workmentioning

confidence: 99%

Discovering Hierarchical Subgraphs of K-Core-Truss

Zhang

et al. 2018

Data Sci. Eng.

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“…k-core [22] is the largest subgraph of a graph in which the degree of each node is at least k. The k-truss [15] model, triangle k-core [27] model and DN-Graph [24] model are defined based on triangles. A k-mutual-friend subgraph model is introduced in [31]. k-edge connected component computation is studied in [26,32,5,9].…”

Section: Related Workmentioning

confidence: 99%

I/O efficient ECC graph decomposition via graph reduction

et al. 2016

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The problem of computing k-edge connected components (kECCs) of a graph G for a specific k is a fundamental graph problem and has been investigated recently. In this paper, we study the problem of ECC decomposition, which computes the k-ECCs of a graph G for all k values. ECC decomposition can be widely applied in a variety of applications such as graph-topology analysis, community detection, Steiner component search, and graph visualization. A straightforward solution for ECC decomposition is to apply the existing k-ECC computation algorithm to compute the k-ECCs for all k values. However, this solution is not applicable to large graphs for two challenging reasons. First, all existing k-ECC computation algorithms are highly memory intensive due to the complex data structures used in the algorithms. Second, the number of possible k values can be very large, resulting in a high computational cost when each k value is independently considered. In this paper, we address the above challenges, and study I/O efficient ECC decomposition via graph reduction. We introduce two elegant graph reduction operators which aim to reduce the size of the graph loaded in memory while preserving the connectivity information of a certain set of edges to be computed for a specific k. We also propose three novel I/O efficient algorithms, Bottom-Up, Top-Down, and Hybrid, that explore the k values in different orders to reduce the redundant computations between different k values. We analyze the I/O and memory costs for all proposed algorithms. In our experiments, we evaluate our algorithms using seven real large datasets with various graph properties, one of which contains 1.95 billion edges. The experimental results show that our proposed algorithms are scalable and efficient.

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“…Moreover, these interactions generate databases that capture a lot of interesting semantics through linkages of social media messages into a rich information network. Visualizing such a rich information network is challenging [43,44,38].…”

Section: Collaborative Visual Analyticsmentioning

confidence: 99%