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
Exact Certification of (Graph) Neural Networks Against Label Poisoning
Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
Chengyu Li, Debo Cheng, Guixian Zhang, Yi Li, Shichao Zhang
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen, Wenqi Fan, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction
João Mattos, Zexi Huang, Mert Kosan, Ambuj Singh, Arlei Silva
Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations
H. Çağrı Bilgi, Lydia Y. Chen, Kubilay Atasu
Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification
Junbo Jacob Lian
Spatial Clustering of Molecular Localizations with Graph Neural Networks
Jesús Pineda, Sergi Masó-Orriols, Joan Bertran, Mattias Goksör, Giovanni Volpe, Carlo Manzo
Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph
Heloisa Oss Boll, Ali Amirahmadi, Amira Soliman, Stefan Byttner, Mariana Recamonde-Mendoza
RL-MILP Solver: A Reinforcement Learning Approach for Solving Mixed-Integer Linear Programs with Graph Neural Networks
Tae-Hoon Lee, Min-Soo Kim
Gradient Inversion Attack on Graph Neural Networks
Divya Anand Sinha, Yezi Liu, Ruijie Du, Yanning Shen
Scale Invariance of Graph Neural Networks
Qin Jiang, Chengjia Wang, Michael Lones, Wei Pang
GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
Yonggai Zhuang, Haoran Chen, Kequan Wang, Teng Fei
Towards Data-centric Machine Learning on Directed Graphs: a Survey
Henan Sun, Xunkai Li, Daohan Su, Junyi Han, Rong-Hua Li, Guoren Wang
FedRGL: Robust Federated Graph Learning for Label Noise
De Li, Haodong Qian, Qiyu Li, Zhou Tan, Zemin Gan, Jinyan Wang, Xianxian Li