Abstract
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
