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  1 - Presenting a model for Multi-layer Dynamic Social Networks to discover Influential Groups based on a combination of Developing Frog-Leaping Algorithm and C-means Clustering
  lida naderloo Mohammad Tahghighi Sharabyan
  20.1001.1.27832570.1400.2.2.6.9
  Introduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, More

  Introduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, simple and multi-layered social networks can be found everywhere and in all fields. The estimation of the importance of each node in these two types of networks is not the same, and weighting the nodes is very necessary to control the network. For this purpose, the relationship between the characteristics of the nodes and the relationship with the network structure should be examined. To find the degree of each node in the system function, parameters like reliability, controllability, and power should be considered. In this paper, a model for dynamic multi-layer social networks to discover influential groups, based on the combination of evolutionary frog jump algorithm and C-means clustering, has been presented.Method: Once data are collected, they were cleaned and normalized so that the desired data leads to the identification of effective individuals and groups. The decision matrix is constructed based on these data. Using this matrix, identification, and clustering (based on fuzzy clustering) were conducted, and the importance of the groups was also determined to determine influential people and groups in social networks. The Jumping frog algorithm was used to improve the detection of influential parameters.Results: In the evaluation and simulation of the clustering part, the proposed method was compared with the K-means method and the balance value of the method in cluster selection was 5. It should be noted that the proposed method showed better improvement compared to the compared methods. Also, the evaluation of the accuracy criterion of the proposed method has improved by 3.3 compared to similar methods and recorded an improvement of 3.3 compared to the basic M-ALCD method.Discussion: In this paper, a multi-layer local community detection model is proposed, which is based on structure and feature information. This model can use the information of the characteristics of the nodes and the information of the strength of similarity that is revealed by social exchanges and improves the accuracy of community detection in Improve multilayer networks. Due to its modularity and computational efficiency, this algorithm performs better on most data sets, unlike the classic multi-layer and global community detection algorithms Manuscript profile