Approaches for finding cohesive subgroups in large‐scale social networks via maximum <i>k</i>‐plex detection

Miao, Zhuqi; Balasundaram, Balabhaskar

doi:10.1002/net.21745

Cited by 18 publications

(10 citation statements)

References 37 publications

(67 reference statements)

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“…Cohesive subgroups include Cliques, N-cliques, N-clan, K-plex, K-core, and Lambda set. [46][47][48].…”

Section: 11mentioning

confidence: 99%

A Review of the Research Progress of Social Network Structure

Huang

et al. 2021

Complexity

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Social network theory is an important paradigm of social structure research, which has been widely used in various fields of research. This paper reviews the development process and the latest progress of social network theory research and analyzes the research application of social network. In order to reveal the deep social structure, this paper analyzes the structure of social networks from three levels: microlevel, mesolevel, and macrolevel and reveals the origin, development, perfection, and latest achievements of complex network models. The regular graph model, P1 model, P2 model, exponential random graph model, small-world network model, and scale-free network model are introduced. In the end, the research on the social network structure is reviewed, and social support network and social discussion network are introduced, which are two important contents of social network research. At present, the research on social networks has been widely used in coauthor networks, citation networks, mobile social networks, enterprise knowledge management, and individual happiness, but there are few research studies on multilevel structure, dynamic research, complex network research, whole network research, and discussion network research. This provides space for future research on social networks.

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“…Cohesive subgroups include Cliques, N-cliques, N-clan, K-plex, K-core, and Lambda set. [46][47][48].…”

Section: 11mentioning

confidence: 99%

A Review of the Research Progress of Social Network Structure

Huang

et al. 2021

Complexity

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“…Some other dense subgraph patterns are proposed, such as k-plex. 26,27 Compared with k-truss and k-core, other subgraph patterns are less popular. However, all these works did not consider the attribute information.…”

Section: Densest Subgraph Miningmentioning

confidence: 99%

“…Wang and Cheng 25 studied truss decomposition algorithm in massive graphs with Map‐reduce, they also improved the efficiency of truss decomposition algorithm in memory. Some other dense subgraph patterns are proposed, such as k ‐plex 26,27 . Compared with k ‐truss and k ‐core, other subgraph patterns are less popular.…”

Section: Related Workmentioning

confidence: 99%

A truss‐based approach for densest homogeneous subgraph mining in node‐attributed graphs

Zhang

Jia

Wang

et al. 2021

Computational Intelligence

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In a wide range of graph analysis tasks such as community detection and event detection, densest subgraph mining is important and primitive. With the development of social network, densest subgraph mining not only need to consider the structural data but also the attributes information, which descripts the features of nodes or edges. However, there are few researches on densest subgraph mining with attribute description.In this article, we only focus on the node-attributed graph. According to the properties of structure and attribute in node-attributed graphs, we define a novel dense subgraph pattern, called hybridized k-truss in attribute-augmented graph. A hybridized k-truss is a subgraph that consists of structural nodes and attribute nodes, of which there are at least (k − 2) common neighbors between any two connected nodes. We introduce the densest hybridized truss problem, and the densest hybridized truss mapping to a densely connected subgraph with homogenous attributes in the original graph. We propose a densest hybridized truss extraction (DHTE) algorithm for node-attributed graphs, to automatically find the densest subgraph with high density and homogenous attributes at the same time. Extensive experimental results of 21 real world datasets demonstrate the effectiveness and efficiency of DHTE over

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“…Compared to the restrictive clique model, k-plex is more suitable for the analysis of the massive graphs encoding from real-world problems, because real-world cohesive subgraphs do not need to meet the rigorous constraint of cliques and could be missing a few connections. Due to the relevance to practical applications, the research attention on k-plex sustainably grows in recent years [Balasundaram et al, 2011;Xiao et al, 2017;Miao and Balasundaram, 2017;Gschwind et al, 2018;Conte et al, 2018;Gao et al, 2018;Zhou et al, 2020;Zhou et al, 2021].…”

Section: Introductionmentioning

confidence: 99%

A New Upper Bound Based on Vertex Partitioning for the Maximum K-plex Problem

Jiang

Zhu

Xie

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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Given an undirected graph, the Maximum k-plex Problem (MKP) is to find a largest induced subgraph in which each vertex has at most k−1 non-adjacent vertices. The problem arises in social network analysis and has found applications in many important areas employing graph-based data mining. Existing exact algorithms usually implement a branch-and-bound approach that requires a tight upper bound to reduce the search space. In this paper, we propose a new upper bound for MKP, which is a partitioning of the candidate vertex set with respect to the constructing solution. We implement a new branch-and-bound algorithm that employs the upper bound to reduce the number of branches. Experimental results show that the upper bound is very effective in reducing the search space. The new algorithm outperforms the state-of-the-art algorithms significantly on real-world massive graphs, DIMACS graphs and random graphs.

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Approaches for finding cohesive subgroups in large‐scale social networks via maximum k‐plex detection

Cited by 18 publications

References 37 publications

A Review of the Research Progress of Social Network Structure

A Review of the Research Progress of Social Network Structure

A truss‐based approach for densest homogeneous subgraph mining in node‐attributed graphs

A New Upper Bound Based on Vertex Partitioning for the Maximum K-plex Problem

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