Graph Neural Network
Graph Neural Networks (GNNs) are a class of machine learning models designed to analyze and learn from data represented as graphs, focusing on capturing relationships between nodes and their impact on downstream tasks like node classification and link prediction. Current research emphasizes improving GNN performance by addressing limitations such as oversmoothing and oversquashing through architectural innovations (e.g., incorporating residual connections, Cayley graph propagation) and novel training techniques (e.g., contrastive learning, Laplacian regularization). GNNs are proving valuable across diverse fields, including social network analysis, drug discovery, and financial modeling, offering powerful tools for analyzing complex relational data where traditional methods fall short.
Papers
GNNAnatomy: Systematic Generation and Evaluation of Multi-Level Explanations for Graph Neural Networks
Hsiao-Ying Lu, Yiran Li, Ujwal Pratap Krishna Kaluvakolanu Thyagarajan, Kwan-Liu Ma
Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN
Benjamin Parlier, Lou Salaün, Hong Yang
On the Expressive Power of Spectral Invariant Graph Neural Networks
Bohang Zhang, Lingxiao Zhao, Haggai Maron
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
Zhonghao Wang, Danyu Sun, Sheng Zhou, Haobo Wang, Jiapei Fan, Longtao Huang, Jiajun Bu
Energy-based Epistemic Uncertainty for Graph Neural Networks
Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition
Lin Zuo, Kunshan Yang, Xianlong Tian, Kunbin He, Yongqi Ding, Mengmeng Jing
Exploiting Global Graph Homophily for Generalized Defense in Graph Neural Networks
Duanyu Li, Huijun Wu, Min Xie, Xugang Wu, Zhenwei Wu, Wenzhe Zhang
Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network
Abubakar Isah, Ibrahim Aliyu, Jaechan Shim, Hoyong Ryu, Jinsul Kim
Decision-focused Graph Neural Networks for Combinatorial Optimization
Yang Liu, Chuan Zhou, Peng Zhang, Shirui Pan, Zhao Li, Hongyang Chen
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
Haoyu Han, Juanhui Li, Wei Huang, Xianfeng Tang, Hanqing Lu, Chen Luo, Hui Liu, Jiliang Tang
Higher Order Structures For Graph Explanations
Akshit Sinha, Sreeram Vennam, Charu Sharma, Ponnurangam Kumaraguru
Graph Neural Network Explanations are Fragile
Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang
Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs
Shuqi He, Jun Zhuang, Ding Wang, Luyao Peng, Jun Song
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
Zihan Luo, Hong Huang, Yongkang Zhou, Jiping Zhang, Nuo Chen, Hai Jin
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs
Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub
GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment
Zhenyu Hou, Haozhan Li, Yukuo Cen, Jie Tang, Yuxiao Dong
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance
Roya Aliakbarisani, Robert Jankowski, M. Ángeles Serrano, Marián Boguñá
RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network
Yunbo Hou, Haoran Ye, Yingxue Zhang, Siyuan Xu, Guojie Song
Temporal Graph Rewiring with Expander Graphs
Katarina Petrović, Shenyang Huang, Farimah Poursafaei, Petar Veličković