I/O efficient ECC graph decomposition via graph reduction

Yuan, Long; Qin, Lu; Lin, Xuemin; Chang, Lijun; Zhang, Wenjie

doi:10.14778/2904483.2904484

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Finally, we would like to consider the application of k -edge-components for graph reduction in future versions of our software. Recent advances in the development of fast algorithms in this area [35,36] make them an appealing addition to our toolkit.…”

Section: Discussionmentioning

confidence: 99%

Fast determination of structurally cohesive subgroups in large networks

Sinkovits

Moody

Oztan

et al. 2016

Journal of Computational Science

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Structurally cohesive subgroups are a powerful and mathematically rigorous way to characterize network robustness. Their strength lies in the ability to detect strong connections among vertices that not only have no neighbors in common, but that may be distantly separated in the graph. Unfortunately, identifying cohesive subgroups is a computationally intensive problem, which has limited empirical assessments of cohesion to relatively small graphs of at most a few thousand vertices. We describe here an approach that exploits the properties of cliques, k-cores and vertex separators to iteratively reduce the complexity of the graph to the point where standard algorithms can be used to complete the analysis. As a proof of principle, we apply our method to the cohesion analysis of a 29,462-vertex biconnected component extracted from a 128,151-vertex co-authorship data set.

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Section: Discussionmentioning

confidence: 99%

Fast determination of structurally cohesive subgroups in large networks

Sinkovits

Moody

Oztan

et al. 2016

Journal of Computational Science

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“…[44] reviews recently advances in maximal clique enumeration. Based on clique, other cohesive subgraph models are also studied, such as k-core [45], k-truss [46], [47], k-edge connected component [48], [49], [50], and (r, s)-nuclei [51], [52].…”

Section: Related Workmentioning

confidence: 99%

Balanced Clique Computation in Signed Networks: Concepts and Algorithms

Chen¹,

Yuan²,

Lin³

et al. 2022

Preprint

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Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. However, in real world, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks. Following the balanced clique model, we study the maximal balanced clique enumeration problem (MBCE) which computes all the maximal balanced cliques in a given signed network. Moreover, in some applications, users prefer a unique and representative balanced clique with maximum size rather than all balanced cliques. Thus, we also study the maximum balanced clique search problem (MBCS) which computes the balanced clique with maximum size. We show that MBCE problem and MBCS problem are both NP-Hard. For the MBCE problem, a straightforward solution is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. For the MBCS problem, we first propose a baseline solution. To overcome the huge search space problem of the baseline solution, we propose a new search framework based on search space partition. To further improve the efficiency of the new framework, we propose multiple optimization strategies regarding to redundant search branches and invalid candidates. We conduct extensive experiments on large real datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms for MBCE problem and MBCS problem.

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“…[14] reviews recently advances in maximal clique enumeration. Based on clique, other cohesive subgraph models are also studied recently, such as k-core [43], ktruss [9,20], k-edge connected component [52,54,59], (r, s)-nuclei [40,41]. Note that our balanced clique model is different from the existing cohesive subgraph models on unsigned networks and it cannot be well solved by the existing works.…”

Section: Related Workmentioning

confidence: 99%

Efficient Maximal Balanced Clique Enumeration in Signed Networks

Zi¹,

Yuan

Lin

et al. 2020

Proceedings of the Web Conference 2020

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Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. In real world, however, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks, and study the maximal balanced clique enumeration problem which computes all the maximal balanced cliques in a given signed network. We show that the maximal balanced clique enumeration problem is NP-Hard. A straightforward solution for the maximal balanced clique enumeration problem is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. We conduct extensive experiments on large real and synthetic datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms.

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I/O efficient ECC graph decomposition via graph reduction

Cited by 12 publications

References 33 publications

Fast determination of structurally cohesive subgroups in large networks

Fast determination of structurally cohesive subgroups in large networks

Balanced Clique Computation in Signed Networks: Concepts and Algorithms

Efficient Maximal Balanced Clique Enumeration in Signed Networks

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