Search Results for author: Yongqiang Chen

Found 4 papers, 2 papers with code

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

1 code implementation ICLR 2022 Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng

Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i. e., Graph Modification Attack (GMA).

Invariance Principle Meets Out-of-Distribution Generalization on Graphs

no code implementations11 Feb 2022 Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

Despite recent developments in using the invariance principle from causality to enable out-of-distribution (OOD) generalization on Euclidean data, e. g., images, studies on graph data are limited.

Out-of-Distribution Generalization

Improving Graph Representation Learning by Contrastive Regularization

no code implementations27 Jan 2021 Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng

Graph representation learning is an important task with applications in various areas such as online social networks, e-commerce networks, WWW, and semantic webs.

Contrastive Learning Graph Representation Learning

Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs

1 code implementation18 Feb 2020 Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng

In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification.

General Classification Node Classification

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