Finding maximal k-edge-connected subgraphs from a large graph

Zhou, Rui; Liu, Chengfei; Yu, Jeffrey Xu; Liang, Weifa; Chen, Baichen; Li, Jianxin

doi:10.1145/2247596.2247652

Cited by 89 publications

(59 citation statements)

References 31 publications

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“…To achieve this, before clustering process, find connected subgraphs, and then start clustering by focus on these subgraphs. one of the best algorithms to find these subgraphs is presented by zhou and liu [13]. A k-edge-connected graph is a connected graph that cannot be disconnected by removing less than k edges [13].…”

Section: Definitionmentioning

confidence: 99%

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SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

HeidariSoureshjani¹,

Delavar²,

Rashidi³

2013

IJCA

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In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented. Previous anonymization algorithms do not consider distribution of nodes in social network graph according to their attributes. SACk tries to focus on this aspect that probability of existence of an edge between two nodes is related to their attributes and this leads to a graph with connected subgraphs. Using connected subgraphs in anonymization process this method obtains better experimental results both in data quality and time. Sequential clustering is used for anonymization using k-edge connected subgraphs for starting step. Sequential clustering is a greedy algorithm and results are dependent on starting point.

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Section: Definitionmentioning

confidence: 99%

“…one of the best algorithms to find these subgraphs is presented by zhou and liu [13]. A k-edge-connected graph is a connected graph that cannot be disconnected by removing less than k edges [13]. This is one of the best structures for extracting connected subgraphs.…”

Section: Definitionmentioning

confidence: 99%

SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

HeidariSoureshjani¹,

Delavar²,

Rashidi³

2013

IJCA

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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%

“…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%

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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“…In the literature, there is a long list of subgraph density measures that may be suited in different application context. Examples include cliques, quasi-cliques [1], k-core, k-edge-connectivity [2], etc. Among these graph density measures, k-core stands out to be the least computationally expensive one that is still giving reasonable results.…”

Section: Introductionmentioning

confidence: 99%

Multi-resolution Social Network Community Identification and Maintenance on Big Data Platform

Aksu

Canim

Chang

et al. 2013

2013 IEEE International Congress on Big Data

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Abstract-Community identification in social networks is of great interest and with dynamic changes to its graph representation and content, the incremental maintenance of community poses significant challenges in computation. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multi-resolution community representation that has to be maintained over time. In this paper, we first formalize this problem using the k-core metric projected at multiple k values, so that multiple community resolutions are represented with multiple k-core graphs. We then present distributed algorithms to construct and maintain a multi-k-core graph, implemented on the scalable big-data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Our algorithms thus enable practitioners to create and maintain communities at multiple resolutions on different topics in rich social network content simultaneously.

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Finding maximal k-edge-connected subgraphs from a large graph

Cited by 89 publications

References 31 publications

SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

I/O efficient ECC graph decomposition via graph reduction

Multi-resolution Social Network Community Identification and Maintenance on Big Data Platform

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