Efficiently computing k-edge connected components via graph decomposition

Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k-influential community based on the concept of k-core, which can capture the influence of a community. Based on the new community model, we propose a linear-time online search algorithm to find the top-r k-influential communities in a network. To further speed up the influential community search algorithm, we devise a linear-space index structure which supports efficient search of the top-r k-influential communities in optimal time. We also propose an efficient algorithm to maintain the index when the network is frequently updated. We conduct extensive experiments on 7 real-world large networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.

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“…The time complexity of the query processing algorithm for both Problem 1 and Problem 2 is linear w.r.t. the answer size 3 , thus it is optimal.…”

Section: Query Processing Algorithmmentioning

confidence: 99%

Influential community search in large networks

et al. 2015

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“…With the proliferation of graph applications, research efforts have been devoted to many fundamental problems in managing and analyzing graph data. Among them, the problem of computing all k-Edge Connected Components (k-ECCs) of a graph for a given k has been recently studied in [26,32,5,9]. Here, a k-ECC of a graph G is a…”

Section: Introductionmentioning

confidence: 99%

“…Computing k-ECCs can be used to identify groups of researchers with similar research interests in a collaboration network (e.g., DBLP). Moreover, k-ECCs computation also plays a role as a building block in many other applications such as the robust detection of communication networks and graph visualization [5,9,24,27].…”

Section: Introductionmentioning

confidence: 99%

“…Given a graph G, a straightforward solution for ECC decomposition is to independently compute the k-ECCs of G for all k values using a k-ECC computation algorithm [26,32,5,9]. However, this solution presents the following two challenges:…”

Section: Introductionmentioning

confidence: 99%

“…In order to compute the k-ECCs of a graph G efficiently, they have to maintain complex data structures that have high memory cost. For example, on the Orkut dataset (a social network) with only 117.2 million edges used in our experiment, the state-of the art algorithm [9] consumes 15.4 GB memory for ECC decomposition. On the other hand, the size of many real-world graphs is huge.…”

Section: Introductionmentioning

confidence: 99%

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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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Chang¹,

Lu²

2018

Cohesive Subgraph Computation Over Large Sparse Graphs

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Efficiently computing k-edge connected components via graph decomposition

Cited by 113 publications

References 19 publications

Influential community search in large networks

Influential community search in large networks

I/O efficient ECC graph decomposition via graph reduction

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