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

Shruti Saxena; Joydeep Chandra

IJCAI 2024poster1 citations

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

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.

Data Mining: DM: ApplicationsData Mining: DM: Mining graphsData Mining: DM: NetworksKnowledge Representation and Reasoning: KRR: ApplicationsMachine Learning: ML: Active learningMachine Learning: ML: Adversarial machine learningMachine Learning: ML: ApplicationsMachine Learning: ML: Representation learningMachine Learning: ML: Sequence and graph learning

BibTeX

@inproceedings{ijcai2024p908,
  title     = {A Survey on Network Alignment: Approaches, Applications and Future Directions},
  author    = {Saxena, Shruti and Chandra, Joydeep},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {8216--8224},
  year      = {2024},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2024/908},
  url       = {https://doi.org/10.24963/ijcai.2024/908},
}