Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation

Aung, Thet Thet; Nyunt, Thi Thi Soe; Cho, Pyae Pyae Win

doi:10.1109/ccoms.2019.8821680

2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) 2019

DOI: 10.1109/ccoms.2019.8821680

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Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation

Thet Thet Aung

Thi Thi Soe Nyunt

Pyae Pyae Win Cho

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2021

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Cited by 2 publications

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“…It also is based on this hypothesis that the performance metaheuristics-based community detection algorithms are influenced directly by the quality function used in the optimization process. In the work by Aung et al [29,30], ABC community detection is used by introducing exclusive mutation and crossover operations. By creating diversity in the new population, these operators try to find optimal similar users to the influential user in each community.…”

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confidence: 99%

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Peng

Rastgari

Navaei

et al. 2021

Mathematical Problems in Engineering

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Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods.

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confidence: 99%

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Peng

Rastgari

Navaei

et al. 2021

Mathematical Problems in Engineering

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Nature inspired link prediction and community detection algorithms for social networks: a survey

Pulipati

Ramasubbareddy

Parvathala

2021

Int J Syst Assur Eng Manag

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Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation

Cited by 2 publications

References 16 publications

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Nature inspired link prediction and community detection algorithms for social networks: a survey

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