@inproceedings{c667cbb24a694d718760f293eb39374d,
title = "Probing Negative Sampling for Contrastive Learning to Learn Graph Representations",
abstract = "Graph representation learning has long been an important yet challenging task for various real-world applications. However, its downstream tasks are mainly performed in the settings of supervised or semi-supervised learning. Inspired by recent advances in unsupervised contrastive learning, this paper is thus motivated to investigate how the node-wise contrastive learning could be performed. Particularly, we respectively resolve the class collision issue and the imbalanced negative data distribution issue. Extensive experiments are performed on three real-world datasets and the proposed approach achieves the SOTA model performance.",
keywords = "Contrastive learning, Graph neural network, Negative sampling",
author = "Shiyi Chen and Ziao Wang and Xinni Zhang and Xiaofeng Zhang and Dan Peng",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 21st Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021 ; Conference date: 13-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1007/978-3-030-86520-7\_27",
language = "英语",
isbn = "9783030865191",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "434--449",
editor = "Nuria Oliver and Fernando P{\'e}rez-Cruz and Stefan Kramer and Jesse Read and Lozano, \{Jose A.\}",
booktitle = "Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Proceedings",
address = "德国",
}