Opinion leader detection in Asian social networks using modified spider monkey optimization

Kumar, Sanjay, Kumar, Akshi ORCID: https://orcid.org/0000-0003-4263-7168, Mallik, Abhishek and Dhall, Sakshi (2023) Opinion leader detection in Asian social networks using modified spider monkey optimization. ACM Transactions on Asian and Low-Resource Language Information Processing, 22 (5). pp. 1-26. ISSN 2375-4699

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Abstract

The Asian social networks are dominated by the society’s collectivist culture, and this interestingly introduces a influence mechanism aided by word-of-mouth and opinion leaders. An opinion leader can help to generate and shape other people’s opinion and achieve a high information spread on any topic. In this work, a modified spider monkey optimization based opinion leader detection approach is proposed. Firstly, we employ the modified node2vec graph embedding to generate the lower dimensional vectors which act as the initial features for the nodes in a typical Asian social network. Next the entire population is broken down into several groups using the k-means++ algorithm where the number of clusters is equal to the number of opinion leaders to be selected. The local and global leaders are chosen by using the coordinates of the cluster centres of these clusters. The coordinates of the centroids of the clusters are then used to detect the local and global leaders in the network. The local leaders then form the seed set of opinion leaders for the network. The positions of the nodes in the network, including the local and global leaders, are updated over a number of iterations. At the end of these iterations, the seed set generating the maximum influence forms the set of opinion leaders in the network. We test our proposed approach using the popular information diffusion and cognitive opinion dynamics (COD) models. We perform intensive experiments on several real-life social networks based on various performance metrics. The results obtained reveal that the proposed approach outperforms several existing techniques of opinion leader detection.