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
MLDGG: Meta-Learning for Domain Generalization on Graphs
Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang, Yujie Lin, Dong Li
Benchmarking Positional Encodings for GNNs and Graph Transformers
Florian Grötschla, Jiaqing Xie, Roger Wattenhofer
Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
Nikhil Garuda, John F. Wu, Dylan Nelson, Annalisa Pillepich
GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
Yiliang Yuan, Mustafa Misir
Graph as a feature: improving node classification with non-neural graph-aware logistic regression
Simon Delarue, Thomas Bonald, Tiphaine Viard
The GECo algorithm for Graph Neural Networks Explanation
Salvatore Calderaro, Domenico Amato, Giosuè Lo Bosco, Riccardo Rizzo, Filippo Vella
Graph Neural Networks on Graph Databases
Dmytro Lopushanskyy, Borun Shi
Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs
Yachao Yang, Yanfeng Sun, Jipeng Guo, Junbin Gao, Shaofan Wang, Fujiao Ju, Baocai Yin
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning
Sanggeon Yun, Ryozo Masukawa, William Youngwoo Chung, Minhyoung Na, Nathaniel Bastian, Mohsen Imani
ScaleNet: Scale Invariance Learning in Directed Graphs
Qin Jiang, Chengjia Wang, Michael Lones, Wei Pang
Gaussian Mixture Models Based Augmentation Enhances GNN Generalization
Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, Amine Mohamed Aboussalah, Michalis Vazirgiannis
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib, Mahathir Mohammad Bappy
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network
Liwei Ni, Xinquan Li, Biwei Xie, Huawei Li
Federated Graph Learning with Graphless Clients
Xingbo Fu, Song Wang, Yushun Dong, Binchi Zhang, Chen Chen, Jundong Li