A MODEL FOR MULTI-LAYER DYNAMIC SOCIAL NETWORKS TO DISCOVER INFLUENTIAL GROUPS BASED ON A COMBINATION OF META-HEURISTIC ALGORITHM AND C-MEANS CLUSTERING
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Volume 5 (1), June 2022, Pages 72-86
Lida Naderlou1 and Zahra Tayyebi Qasabeh2
Science and technology are proliferating, and complex networks have become a necessity in our daily life, so separating people from complex networks built on the fundamental needs of human life is almost impossible. This research presented a multi-layer dynamic social networks model to discover influential groups based on a developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, we used the matrix to identify and cluster (based on phase clustering) and determined each group’s importance. The frog-leaping algorithm was used to improve the identification of influence parameters, which led to improvement in node’s importance, to discover influential individuals and groups in social networks, In the measurement and simulation of clustering section, the proposed method was contrasted against the K-means method, and its equilibrium value in cluster selection resulted from 5. The proposed method presented a more genuine improvement compared to the other methods. However, measuring precision indicators for the proposed method had a 3.3 improvement compared to similar methods and a 3.8 improvement compared to the M-ALCD primary method.
Multi-layer Dynamic Social Networks, Influential Groups, Meta-Heuristic Algorithm, C-means Clustering.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, 10(2-3), 191-203.
Dasgupta, S., & Prakash, C. (2016, March). Intelligent detection of influential nodes in networks. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 2626-2628). IEEE.
Hafiene, N., Karoui, W., & Romdhane, L. B. (2020). An incremental approach to update influential nodes in dynamic social networks. Procedia Computer Science, 176, 781-790.
Huynh, T. H. (2008, April). A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers. In 2008 IEEE International Conference on Industrial Technology (pp. 1-6). IEEE.
Interdonato, R., Tagarelli, A., Ienco, D., Sallaberry, A., & Poncelet, P. (2017). Local community detection in multilayer networks. Data Mining and Knowledge Discovery, 31(5), 1444-1479.
Lei, Y., Zhou, Y., & Shi, J. (2019). Overlapping communities detection of social network based on hybrid C-means clustering algorithm. Sustainable Cities and Society, 47, 101436.
Li, X., Xu, G., Jiao, L., Zhou, Y., & Yu, W. (2019). Multi-layer network community detection model based on attributes and social interaction intensity. Computers & Electrical Engineering, 77, 300-313.
Mittal, R., & Bhatia, M. P. S. (2019). Classifying the influential individuals in multi-layer social networks. International Journal of Electronics, Communications, and Measurement Engineering (IJECME), 8(1), 21-32.
Noori, A. (2022). A New Method for Detecting Influential Nodes in Social Network Graphs Using Deep Learning Techniques. Karafan Quarterly Scientific Journal.
Qian, Y., & Pan, L. (2021, October). Data-Driven Influential Nodes Identification in Dynamic Social Networks. In International Conference on Collaborative Computing: Networking, Applications and Worksharing (pp. 592-607). Springer, Cham.
Vakili, M., Ghamsari, M., & Rezaei, M. (2020). Performance analysis and comparison of machine and deep learning algorithms for IoT data classification. arXiv preprint arXiv:2001.09636.
Wang, B., Zhang, J., Dai, J., & Sheng, J. (2022). Influential nodes identification using network local structural properties. Scientific Reports, 12(1), 1-13.
Yang, Y., & Xie, G. (2016). Efficient identification of node importance in social networks. Information Processing & Management, 52(5), 911-922.
Zhou, J., Zhang, Y., & Cheng, J. (2014). Preference-based mining of top-K influential nodes in social networks. Future Generation Computer Systems, 31, 40-47.