A Survey on Network Alignment: Approaches, Applications and Future Directions

A Survey on Network Alignment: Approaches, Applications and Future Directions

Shruti Saxena, Joydeep Chandra

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Survey Track. Pages 8216-8224. https://doi.org/10.24963/ijcai.2024/908

Network alignment, the task of mapping corresponding nodes across networks, is attracting more attention for cross-network analysis in diverse domains, including social, biological, and co-authorship networks. Although a variety of methods have been proposed, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of network alignment, characterizing existing approaches, and then systematically summarizing and reviewing their performance and highlighting their scopes for future development. Finally, we discuss some important applications and give directions for future research within this domain.
Keywords:
Data Mining: DM: Applications
Data Mining: DM: Mining graphs
Data Mining: DM: Networks
Knowledge Representation and Reasoning: KRR: Applications
Machine Learning: ML: Active learning
Machine Learning: ML: Adversarial machine learning
Machine Learning: ML: Applications
Machine Learning: ML: Representation learning
Machine Learning: ML: Sequence and graph learning