@inproceedings{li-etal-2023-grenade,
title = "{GRENADE}: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs",
author = "Li, Yichuan and
Ding, Kaize and
Lee, Kyumin",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.181",
doi = "10.18653/v1/2023.findings-emnlp.181",
pages = "2745--2757",
abstract = "Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately. However, existing methods either struggle to capture the full extent of structural context information or rely on task-specific training labels, which largely hampers their effectiveness and generalizability in practice. To solve the problem of self-supervised representation learning on text-attributed graphs, we develop a novel Graph-Centric Language model {--} GRENADE. Specifically, GRENADE harnesses the synergy of both pre-trained language model and graph neural network by optimizing with two specialized self-supervised learning algorithms: graph-centric contrastive learning and graph-centric knowledge alignment. The proposed graph-centric self-supervised learning algorithms effectively help GRENADE to capture informative textual semantics as well as structural context information on text-attributed graphs. Through extensive experiments, GRENADE shows its superiority over state-of-the-art methods.",
}
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<abstract>Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately. However, existing methods either struggle to capture the full extent of structural context information or rely on task-specific training labels, which largely hampers their effectiveness and generalizability in practice. To solve the problem of self-supervised representation learning on text-attributed graphs, we develop a novel Graph-Centric Language model – GRENADE. Specifically, GRENADE harnesses the synergy of both pre-trained language model and graph neural network by optimizing with two specialized self-supervised learning algorithms: graph-centric contrastive learning and graph-centric knowledge alignment. The proposed graph-centric self-supervised learning algorithms effectively help GRENADE to capture informative textual semantics as well as structural context information on text-attributed graphs. Through extensive experiments, GRENADE shows its superiority over state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs
%A Li, Yichuan
%A Ding, Kaize
%A Lee, Kyumin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-grenade
%X Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately. However, existing methods either struggle to capture the full extent of structural context information or rely on task-specific training labels, which largely hampers their effectiveness and generalizability in practice. To solve the problem of self-supervised representation learning on text-attributed graphs, we develop a novel Graph-Centric Language model – GRENADE. Specifically, GRENADE harnesses the synergy of both pre-trained language model and graph neural network by optimizing with two specialized self-supervised learning algorithms: graph-centric contrastive learning and graph-centric knowledge alignment. The proposed graph-centric self-supervised learning algorithms effectively help GRENADE to capture informative textual semantics as well as structural context information on text-attributed graphs. Through extensive experiments, GRENADE shows its superiority over state-of-the-art methods.
%R 10.18653/v1/2023.findings-emnlp.181
%U https://aclanthology.org/2023.findings-emnlp.181
%U https://doi.org/10.18653/v1/2023.findings-emnlp.181
%P 2745-2757
Markdown (Informal)
[GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs](https://aclanthology.org/2023.findings-emnlp.181) (Li et al., Findings 2023)
ACL