96 citations · 415 across the 46 of their papers we have counts for
6 papers · 1 filter
A Survey on Explainability of Graph Neural Networks
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma +2
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, includ…
Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, Feng Shi +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming fr…
Sharpness-Aware Data Poisoning Attack
Pengfei He, Han Xu, Jie Ren +4
Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' trai…
Counterfactual Learning on Graphs: A Survey
Zhimeng Guo, Teng Xiao, Zongyu Wu +3
Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success i…
Heterogeneous Social Event Detection via Hyperbolic Graph Representations
Zitai Qiu, Jia Wu, Jian Yang +2
Social events reflect the dynamics of society and, here, natural disasters and emergencies receive significant attention. The timely detection of these events can provide organisat…
State of the Art and Potentialities of Graph-level Learning
Zhenyu Yang, Ge Zhang, Jia Wu +9
Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as inp…