Forbidden Nodes Aware Community Search

Wang, Chaokun; Zhu, Junchao

doi:10.1609/aaai.v33i01.3301758

Cited by 12 publications

(2 citation statements)

References 22 publications

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“…The notion of -truss [6] has been the focus of many works which aim at detecting a maximal -truss for each (e.g., [7]), or a -truss containing certain nodes and/or attributes (e.g., [13,14]). There is also a considerable amount of work on extending the notion oftruss to capture application-specific requirements (e.g., [9,14,22]).…”

Section: Related Workmentioning

confidence: 99%

On Breaking Truss-Based Communities

Chen

Conte

Grossi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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A -truss is a graph such that each edge is contained in at least − 2 triangles. This notion has attracted much attention, because it models meaningful cohesive subgraphs of a graph. We introduce the problem of identifying a smallest edge subset of a given graph whose removal makes the graph -truss-free. We also introduce a problem variant where the identified subset contains only edges incident to a given set of nodes and ensures that these nodes are not contained in any -truss. These problems are directly applicable in communication networks: the identified edges correspond to vital network connections; or in social networks: the identified edges can be hidden by users or sanitized from the output graph. We show that these problems are NP-hard. We thus develop exact exponentialtime algorithms to solve them. To process large networks, we also develop heuristics sped up by an efficient data structure for updating the truss decomposition under edge deletions. We complement our heuristics with a lower bound on the size of an optimal solution to rigorously evaluate their effectiveness. Extensive experiments on 10 real-world graphs show that our heuristics are effective (close to the optimal or to the lower bound) and also efficient (up to two orders of magnitude faster than a natural baseline).

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Section: Related Workmentioning

confidence: 99%

On Breaking Truss-Based Communities

Chen

Conte

Grossi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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show abstract

“…Community search finds the query-dependent communities in a graph [6,14,19]. Community search models can be categorized based on different dense subgraphs including 𝑘-core [2,11,26,27,36,40], 𝑘-truss [20], quasi-clique [10], and densest subgraph [43]. Sozio and Gionis defined the problem of community search and proposed a 𝑘-core based model with the distance and size constraints [36].…”

Section: Related Workmentioning

confidence: 99%

Butterfly-Core Community Search over Labeled Graphs

Dong¹,

Huang²,

Yuan³

et al. 2021

Preprint

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Community search aims at finding densely connected subgraphs for query vertices in a graph. While this task has been studied widely in the literature, most of the existing works only focus on finding homogeneous communities rather than heterogeneous communities with different labels. In this paper, we motivate a new problem of cross-group community search, namely Butterfly-Core Community (BCC), over a labeled graph, where each vertex has a label indicating its properties and an edge between two vertices indicates their cross relationship. Specifically, for two query vertices with different labels, we aim to find a densely connected cross community that contains two query vertices and consists of butterfly networks, where each wing of the butterflies is induced by a k-core search based on one query vertex and two wings are connected by these butterflies. Indeed, the BCC structure admits the structure cohesiveness and minimum diameter, and thus can effectively capture the heterogeneous and concise collaborative team. Moreover, we theoretically prove this problem is NP-hard and analyze its non-approximability. To efficiently tackle the problem, we develop a heuristic algorithm, which first finds a BCC containing the query vertices, then iteratively removes the farthest vertices to the query vertices from the graph. The algorithm can achieve a 2-approximation to the optimal solution. To further improve the efficiency, we design a butterfly-core index and develop a suite of efficient algorithms for butterfly-core identification and maintenance as vertices are eliminated. Extensive experiments on seven real-world networks and four novel case studies validate the effectiveness and efficiency of our algorithms.

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A comprehensive overview of fake news detection on social networks

Sharma,

Singh

2024

Soc. Netw. Anal. Min.

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Forbidden Nodes Aware Community Search

Cited by 12 publications

References 22 publications

On Breaking Truss-Based Communities

On Breaking Truss-Based Communities

Butterfly-Core Community Search over Labeled Graphs

A comprehensive overview of fake news detection on social networks

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