Graph Neural Networks

Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links …

Zicheng Zhao, Linhao Luo, Shirui Pan, Quoc Viet Hung Nguyen, Chen Gong

Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification

Xiao Shen, Mengqiu Shao, Shirui Pan, Laurence Yang, Xi Zhou

Learning Strong Graph Neural Networks with Weak Information

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs …

Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan

Boosting Graph Contrastive Learning via Adaptive Sampling

Contrastive Learning (CL) is a prominent technique for self-supervised representation learning, which aims to contrast semantically …

Sheng Wan, Yibing Zhan, Shuo Chen, Shirui Pan, Jian Yang, Dacheng Tao, Chen Gong

G2Pxy: Generative Open-Set node Classification on Graphs with Proxy Unknowns

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural …

Qin Zhang, Ze Lin Shi, Xiaolin Zhang, Xiaojun Chen, Philippe Fournier-Viger, Shirui Pan

Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in real-world applications, they have been shown …

He Zhang, Bang Wu, Shuo Wang, Xiangwen Yang, Minhui Xue, Shirui Pan, Xingliang Yuan

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophilic-prone or …

Yizhen Zheng, He Zhang, Vincent Lee, Yu Zheng, Xiao Wang, Shirui Pan

Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, …

Linhao Luo, Reza Haffari, Yuan-Fang Li, Shirui Pan

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

Recent studies focus on formulating the traffic forecasting as a spatio-temporal graph modeling problem. They typically construct a …

Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu

Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with …

Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan