Summary of Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions, by Xiao Yang et al.
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directionsby Xiao Yang, Gaolei Li, Jianhua LiFirst…
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directionsby Xiao Yang, Gaolei Li, Jianhua LiFirst…
HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningby Zhuoning Guo, Duanyi Yao, Qiang…
Geodesic Distance Between Graphs: A Spectral Metric for Assessing the Stability of Graph Neural Networksby…
Differentiable Reasoning about Knowledge Graphs with Region-based Graph Neural Networksby Aleksandar Pavlovic, Emanuel Sallinger, Steven…
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarseningby Guy Bar-Shalom,…
Transformers meet Neural Algorithmic Reasonersby Wilfried Bounsi, Borja Ibarz, Andrew Dudzik, Jessica B. Hamrick, Larisa…
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationby Yuankai Luo, Lei Shi, Xiao-Ming…
Introducing Diminutive Causal Structure into Graph Representation Learningby Hang Gao, Peng Qiao, Yifan Jin, Fengge…
Conformal Load Prediction with Transductive Graph Autoencodersby Rui Luo, Nicolo ColomboFirst submitted to arxiv on:…
GraphFM: A Comprehensive Benchmark for Graph Foundation Modelby Yuhao Xu, Xinqi Liu, Keyu Duan, Yi…