Identifying overlapping communities in networks using evolutionary method

Zhan, Wenfeng; Guan, Jihong; Chen, Huahui; Niu, Jun; Jin, Guang

doi:10.1016/j.physa.2015.09.031

Cited by 11 publications

(3 citation statements)

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“…These algorithms are either single‐objective or multi‐objective. Zhan et al (2016) proposed a coding scheme with two segments. They utilized a single‐objective evolutionary algorithm to uncover overlapping communities.…”

Section: Related Workmentioning

confidence: 99%

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

2021

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Complex networks represent various real-world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed.Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real-world known networks, including Zachary, Dolphins, and COVID-19 Co-authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state-of-the-art overlapping community detection algorithms in terms of recall, precision, F-score and overlapping normalized mutual information.

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Section: Related Workmentioning

confidence: 99%

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

2021

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“…However, in real networks, nodes are often shared between communities, which lead to overlapping communities [9]. In the last two years, overlapping-community detection has become quite popular [10], [11]. Zhou et al [10] presented an ant colony based overlapping community detection algorithm, which mainly includes ants' location initialization and movement.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al [10] presented an ant colony based overlapping community detection algorithm, which mainly includes ants' location initialization and movement. Zhan et al [11] developed an effective encoding scheme for overlapping communities, and introduced two measures for the informativeness of nodes.…”

Section: Introductionmentioning

confidence: 99%

Community Detection in Weighted Networks via Recursive Edge-Filtration

Shen¹,

Liu²,

Xing³

2016

JCM

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In this paper, we present a Weighted Filtration Coefficient (WFC)  and a corresponding filtration method to detect the communities in weighted networks. In our method, a weighted network can be divided into groups by recursive filtration operations, and the dividing results are evaluated by the. We prove that optimization on local  enables us to obtain maximal global weighted modularity w Q , which corresponds to the correct communities. For a weighted network with m edges and c communities, the weighted communities can be detected in time ((1))  O c m , which is in linear scale time with the number of edges. Furthermore, the local weighted communities can be detected in an increasing order according to the edge weights between them. This division can reveal different levels of close connections between the nodes.

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