Overlapping communities from lines and triangles in complex networks

Gao, Yang; Zhang, Hongli; Zhang, Yue

doi:10.1016/j.physa.2019.01.074

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Gao et al 7 proposed VISM—a similarity‐based seeding approach that combines similarity and vertex influence to search for lines and triangles in the network and use its grain for discovering communities. Also, they removed the subgraphs; their size is smaller than a specific scale.…”

Section: Analysis Of Related Workmentioning

confidence: 99%

Community detection and service discovery on Social Internet of Things

Rad

Rahmani

Sahafi

et al. 2023

Int J Communication

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Summary Due to the increasing growth of objects and problems such as increased traffic, overload, delay in response, and low search volume in the service discovery process in the complex Social Internet of Things (SIoT) environment, we provide an effective mechanism in the service discovery process by grouping objects based on common criteria that help us improve service search performance. In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities.

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Section: Analysis Of Related Workmentioning

confidence: 99%

Community detection and service discovery on Social Internet of Things

Rad

Rahmani

Sahafi

et al. 2023

Int J Communication

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“…This kind of algorithms have a obvious shortcoming, that is, it has a large time complexity, resulting in a huge time cost. Compared with the global network algorithm, the local network algorithm does not need all the information of the network, and can only mine the local community division of the network from the local structure of the network, which effectively reduces the time cost 2 . This paper selects the local expansion function algorithm (LFM), and proposes an overlapping community detecion algorithm SCF-LFM, which improves the seed selection method and the community expansion method.…”

Section: Introductionmentioning

confidence: 99%

Seeding based on complex factors-local fitness method

Yang

Dai

2023

Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023)

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This paper studies the static network community detection algorithm, improves the overlapping community detection algorithm LFM based on local expansion, and improves the randomness and redundancy of this algorithm in the process of seed selection and community expansion. Vertex influence and their similarity are used to select seeds, and the redundant steps in community expansion are optimized, and the SCF-LFM algorithm is proposed. The experimental results show that the algorithm has improved accuracy and modularity, and the result of the division has a high degree of community.

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A community detection algorithm based on Quasi-Laplacian centrality peaks clustering

Shi

Ding

et al. 2021

Appl Intell

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Overlapping communities from lines and triangles in complex networks

Cited by 5 publications

References 27 publications

Community detection and service discovery on Social Internet of Things

Community detection and service discovery on Social Internet of Things

Seeding based on complex factors-local fitness method

A community detection algorithm based on Quasi-Laplacian centrality peaks clustering

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