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} }