Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

Wang, Yanjiao; Song, Jiaxu; Sun, Peng

doi:10.1109/access.2022.3168714

Cited by 4 publications

(3 citation statements)

References 56 publications

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“…We here reviewed a variety of initialization methods widely used in CD for complex networks. It is worth noting that there exist some competitive initializaiton methods for the CD, such as label propagation algorithm (LPA)-based initialization methods [34,35] and locus-based adjacency representation (LAR)-based initialization methods [1,20,36,37].…”

Section: Initialization Methods For the CDmentioning

confidence: 99%

“…Two strategies are utilized to improve the quality and diversity of the initial population. The first solutions are generated as the adjacent node-based initialization method [37]. In this initialization method, individual nodes are encoded using the neighboring node, which is selected randomly from the current node; then, the current node and its neighboring node are aggregated in the same community by decoding.…”

Section: Fast Elite Population Initializationmentioning

confidence: 99%

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An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Kang

Wang

et al. 2022

Mathematics

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Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

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Section: Initialization Methods For the CDmentioning

confidence: 99%

Section: Fast Elite Population Initializationmentioning

confidence: 99%

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Kang

Wang

et al. 2022

Mathematics

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show abstract

“…Te community detection methods change gradually with the increasing complexity of network data. Scholars always tend to develop community detection methods that can extract more and deeper useful information from the network to guide community division [24]. Currently available community detection algorithms are mainly designed for directed networks, undirected networks, and networks with weighted graphs, and they divide diferent communities by information provided by the network structure [25].…”

Section: Community Detectionmentioning

confidence: 99%

Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China

Yao

Xiao

2023

Discrete Dynamics in Nature and Society

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Air transport has broad development potential along the “ Belt and Road” by owing to its fast, direct, and comfortable advantages, and the community structure is an important factor determining the efficiency and service functions of the aviation network. The community detection and community structure analysis of the “Belt and Road” aviation network would help to understand the internal relationship of the “Belt and Road” aviation network spatial structure and better integrated and optimized the network. Based on the data of international routes between 522 airports along the “Belt and Road” in 2019, this paper establishes an aviation network model and calculates the degree, average path, clustering coefficient, and centrality index of the “Belt and Road” aviation network. Through Louvain community detection method, 522 airports in the aviation network were divided into four communities, and the structural characteristics and geographical distribution characteristics of the community network were analyzed. The key node identification model in the community is constructed, the hierarchy of each community network is analyzed using the k-core decomposition method, the core node set of the community network is found, the core boundary node set of four aviation community networks is identified, the core node set and core boundary nodes are analyzed repeatedly, and the key node set of the whole network is obtained. Finally, from the perspective of China, a deep integration scheme between Chinese airports, their communities, and other key node sets was proposed. The research found that there are four associations in the “Belt and Road” aviation network: West Asia and North Africa, Russia, Central and Eastern Europe, and China and Southeast Asia. Chinese and Southeast Asian societies rank third among the four societies in terms of scale. They not only have the largest number of key nodes but also the lowest repetition rate of the key nodes. The international airline links in the “Belt and Road” aviation network are more complex. A total of 118 key nodes were identified, of which 83.1% were in the top 20% of overall network centrality.

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Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks

Zhang,

Shang,

et al. 2023

IEEE Access

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Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

Cited by 4 publications

References 56 publications

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China

Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks

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