Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Author:

Kumar Meena Sunil1ORCID,Sheshar Singh Shashank2ORCID,Singh Kuldeep1ORCID

Affiliation:

1. Department of Computer Science, University of Delhi, New Delhi, India

2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, India

Abstract

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, etc. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Publisher

Association for Computing Machinery (ACM)

Reference61 articles.

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