Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning.
Lingwen Liu|Guangqi Wen|Peng Cao|Jinzhu Yang|Weiping Li|Osmar R. Zaïane
| Anthology ID: | DBLP:conf/wsdm/LiuW0YLZ24 |
|---|---|
| Volume: | Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024 |
| Year: | 2024 |
| Venue: | Web Search and Data Mining (WSDM) |
| Publisher: | ACM |
| Pages: | 443-451 |
| URL: | https://doi.org/10.1145/3616855.3635765 |
| DOI: | https://doi.org/10.1145/3616855.3635765 |
| DBLP: | conf/wsdm/LiuW0YLZ24 |
| BibTeX: |
@inproceedings{liu-2024-capturing,
author = {Lingwen Liu and
Guangqi Wen and
Peng Cao and
Jinzhu Yang and
Weiping Li and
Osmar R. Za\"{i}ane},
editor = {Luz Angelica Caudillo-Mata and
Silvio Lattanzi and
Andr\'{e}s Mu\~{n}oz Medina and
Leman Akoglu and
Aristides Gionis and
Sergei Vassilvitskii},
title = {{Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning}},
booktitle = {{Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024}},
pages = {443--451},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3616855.3635765},
doi = {https://doi.org/10.1145/3616855.3635765}
}