Let Invariant Rationale Discovery Inspire Graph Contrastive Learning

1 code implementation • 16 Jun 2022 • Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua

Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.

Contrastive Learning

Reinforced Causal Explainer for Graph Neural Networks

1 code implementation • 23 Apr 2022 • Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua

Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.

Graph Classification

Towards Multi-Grained Explainability for Graph Neural Networks

1 code implementation • NeurIPS 2021 • Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.

Knowledge-Aware Meta-learning for Low-Resource Text Classification

1 code implementation • EMNLP 2021 • Huaxiu Yao, Yingxin Wu, Maruan Al-Shedivat, Eric P. Xing

Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task.

Meta-Learning Sentence +2

Causal Screening to Interpret Graph Neural Networks

no code implementations • 1 Jan 2021 • Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.

Explanation Generation