1

Securing Graph Neural Networks in MLaaS: A Comprehensive Realisation of Query-based Integrity Verification

Bang Wu, Xingliang Yuan, Shuo Wang, Qi Li, Minhui Xue, Shirui Pan

GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks

The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms …

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

GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as …

Xin Zheng, Miao Zhang, Chunyang Chen, Soheila Molaei, Chuan Zhou, Shirui Pan

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, …

Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan

Towards Self-Interpretable Graph-Level Anomaly Detection

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a …

Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

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

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

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