Index-Based Densest Clique Percolation Community Search in Networks

Yuan, Long; Qin, Lu; Zhang, Wenjie; Chang, Lijun; Yang, Jianye

doi:10.1109/tkde.2017.2783933

Cited by 119 publications

(45 citation statements)

References 37 publications

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“…In this section, we survey the CS solutions [45,205,195,187,125] using k-clique model. We can divide them into two groups, where the first group [45,205,195,187] focuses on simple graphs, while the second group [125] is developed for attributed graphs.…”

Section: Discussionmentioning

confidence: 99%

“…Simple graphs Attributed graphs Keyword Location Temporal Influence (weight) Profile k-core [175,46,15,66] (P. 1, 2, 3, 4, 5) [61,58] (P. 6) [60,65,185,221] (P. 7,8,9) [129] (P. 10) [127,128,30,215,21,126] (P. 12,13) [31] (P. 14) k-truss [98,6,101] (P. 15,16) [102] (P. 17) -- [216] (P. 18) k-clique [45,205,195,187] (P. 19,20,21,22) -- [125] Example 2 Let us reconsider the graph G in Fig. 2(a).…”

Section: Metricmentioning

confidence: 99%

“…Following the k-clique community model in [151], Yuan et al studied the problem of densest clique percolation community search [205], where a k-clique percolation community (KCPC) is a k-clique component in [151]. In particular, they aimed to find the k-clique percolation community with the maximum k value that contains a given set of query vertices.…”

Section: Densest Clique Percolation Community Searchmentioning

confidence: 99%

“…Fig. 21 in [205] illustrates a part of the collaboration network in DBLP, in which each vertex represents an author and each edge indicates the co-author relationship between two authors. G 1 is a 4-clique percolation community as it is a maximal union of five adjacent 4-cliques: 15 , v 16 , v 17 , v 18 }, and any two 4-cliques share 3 nodes.…”

Section: Problem 20mentioning

confidence: 99%

“…If there is no KCPC detected, the k value will be decreased by one until a KCPC is detected. To efficiently support online DCPC search, an index-based approach is developed in [205]. Particularly, based on the observation that a k-clique component can be treated as a union of maximal cliques, they take maximal cliques as building blocks of k-clique components and propose a tree-structure named clique adjacency tree which can efficiently identify the k-clique components for a given k value.…”

Section: Problem 20mentioning

confidence: 99%

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A survey of community search over big graphs

et al. 2019

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With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works.In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze

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Section: Discussionmentioning

confidence: 99%

Section: Metricmentioning

confidence: 99%

Section: Densest Clique Percolation Community Searchmentioning

confidence: 99%

Section: Problem 20mentioning

confidence: 99%

Section: Problem 20mentioning

confidence: 99%

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A survey of community search over big graphs

et al. 2019

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Keyword-Centric Community Search over Large Heterogeneous Information Networks

Qiao

Zhang

Yuan

et al. 2021

Database Systems for Advanced Applications

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Significant Engagement Community Search on Temporal Networks

Zhang

Lin

Yuan

et al. 2022

Database Systems for Advanced Applications

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Community search, retrieving the cohesive subgraph which contains the query vertex, has been widely touched over the past decades. The existing studies on community search mainly focus on static networks. However, real-world networks usually are temporal networks where each edge is associated with timestamps. The previous methods do not work when handling temporal networks. We study the problem of identifying the significant engagement community to which the user-specified query belongs. Specifically, given an integer k and a query vertex u, then we search for the subgraph H which satisfies (i) u ∈ H; (ii) the de-temporal graph of H is a connected k-core; (iii) In H that u has the maximum engagement level. To address our problem, we first develop a top-down greedy peeling algorithm named TDGP, which iteratively removes the vertices with the maximum temporal degree. To boost the efficiency, we then design a bottom-up local search algorithm named BULS and its enhanced versions BULS+ and BULS*. Lastly, we empirically show the superiority of our proposed solutions on six real-world temporal graphs.

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Index-Based Densest Clique Percolation Community Search in Networks

Cited by 119 publications

References 37 publications

A survey of community search over big graphs

A survey of community search over big graphs

Keyword-Centric Community Search over Large Heterogeneous Information Networks

Significant Engagement Community Search on Temporal Networks

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