Affiliation:
1. Center for Excellence in Information Technology Institute of Management Sciences Peshawar Pakistan
2. Departamento de Ciência e Tecnologia Zayed University Abu Dhabi UAE
3. REMIT, IJP Universidade Portucalense Porto Portugal
4. IEETA Universidade de Aveiro Aveiro Portugal
Abstract
SummaryThis paper discusses the importance of feature extraction and structure similarity measurement in the analysis of complex networks. Social networks, biological systems, and transportation networks are just a few examples of the many phenomena that have been modeled using complex networks. However, analyzing these networks can be challenging due to their large size and complexity. Feature extraction techniques can help to simplify the network by identifying key nodes or substructures. Structure similarity measurement techniques can be used to compare different networks and identify similarities and differences between them. Previous research has suggested that real‐world complex networks are influenced by multiplex features and either local or global features. However, the interaction between these characteristics is not well understood. The proposed approach outperforms other graph similarity methods on publicly available datasets, with accurate estimations of overall complex network structures. Specifically, the approach based on cosine similarity outperforms as compared to existing methods. Overall, this study highlights the importance of considering various graph features–local and global features and their interactions in the analysis of complex networks.
Subject
Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering
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