利用蝙蝠优化算法发现社交网络上的最大k-clique

Q1 Mathematics

Computational Social Networks Pub Date : 2021-02-02 DOI:10.1186/s40649-021-00087-y

Akram Khodadadi, Shahram Saeidi

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引用次数: 0

摘要

k-团问题是识别网络上大小为k的最大完整子图的问题，它在社会网络分析(SNA)、编码理论、几何等领域有许多应用。由于问题的np完全性质，元启发式方法引起了研究人员的兴趣，并开发了一些算法。本文提出了一种基于Bat优化方法的新算法，用于寻找社交网络上的最大k-clique，以提高收敛速度和精度、召回率和f1分数等评价标准。该算法在Matlab®软件中对Dolphin社交网络和DIMACS数据集进行了仿真，k = 3,4,5。计算结果表明，与遗传算法(GA)和蚁群优化(ACO)方法相比，前者在数据集上的收敛速度有所提高。此外，对后一个数据集的评价标准也进行了修改，当k = 5时，f1得分为100%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Discovering the maximum k-clique on social networks using bat optimization algorithm

The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.