A fast local community detection algorithm in complex networks

Tang, Zhikang; Tang, Yong; Li, Chunying; Cao, Jun; Chen, Guohua; Lin, Ronghua

doi:10.1007/s11280-021-00931-1

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…This method is remarkably stable. In [11], a fast local community detection algorithm is proposed that can detect large-scale communities. This method reduces computation costs and improves stability.…”

Section: Introductionmentioning

confidence: 99%

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Ranjkesh,

Masoumi,

Hashemi

2024

World Wide Web

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Networks in the real world are dynamic and evolving. The most critical process in networks is to determine the structure of the community, based on which we can detect hidden communities in a complex network. The design of strong network structures is of great importance, meaning that a system must maintain its function in the face of attacks and failures and have a strong community structure. In this paper, we proposed the robust memetic algorithm and used the idea to optimize the detection of dynamic communities in complex networks called RDMA_NET (Robust Dynamic Memetic Algorithm). In this method, we work on dynamic data that affects the two main parts of the initial population value and the calculation of the evaluation function of each population, and there is no need to determine the number of communities in advance. We used two sets of real-world networks and the LFR dataset. The results show that our proposed method, RDMA_Net, can nd a better solution than modern approaches and provide near-optimal performance in search of network topologies with a strong community structure.

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Section: Introductionmentioning

confidence: 99%

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Ranjkesh,

Masoumi,

Hashemi

2024

World Wide Web

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Motif‐based embedding label propagation algorithm for community detection

Tang

et al. 2021

Int J of Intelligent Sys

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Community detection can exhibit the aggregation behavior of complex networks. Network motifs are the fundamental building blocks which can reveal the higher‐order structure of complex networks. Label propagation algorithm has the advantage of approximately linear time complexity, unfortunately, the randomness of label update is a major but unsolved issue. For these reasons, this paper proposes a novel community detection method, named motif‐based embedding label propagation algorithm (MELPA). First, complex network topology is reconstructed by merging higher‐order topology with lower‐order connectivity features, where higher‐order topology is captured by mining network motifs. Second, We design a label propagation characteristic model according to nodes influence, then a new label update rule is formulated based on reconstructed weighted network, the rule integrates frequency among neighbor labels, influence of nodes, propagation characteristics and closeness of nodes to update the node label, the purpose is to overcome the randomness of label selection and identify a better and more stable community structure. Finally, extensive experiments on synthetic networks and real‐world complex networks are conducted to verify the effectiveness of MELPA, especially for the complex networks with unobvious community structure, MELPA will get unexpected results.

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