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
GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval
Dong Liu, Roger Waleffe, Meng Jiang, Shivaram Venkataraman
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Juan Cervino, Md Asadullah Turja, Hesham Mostafa, Nageen Himayat, Alejandro Ribeiro
Distance Recomputator and Topology Reconstructor for Graph Neural Networks
Dong Liu, Meng Jiang
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
Mahdi Mohammadizadeh, Arash Mozhdehi, Yani Ioannou, Xin Wang
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
Yicheng Zhou, Pengfei Wang, Hao Dong, Denghui Zhang, Dingqi Yang, Yanjie Fu, Pengyang Wang
Root Cause Analysis of Anomalies in 5G RAN Using Graph Neural Network and Transformer
Antor Hasan, Conrado Boeira, Khaleda Papry, Yue Ju, Zhongwen Zhu, Israat Haque
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Mucong Ding, Tahseen Rabbani, Bang An, Evan Z Wang, Furong Huang
Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
Zesheng Liu, Maryam Rahnemoonfar
Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini
Efficient Graph Similarity Computation with Alignment Regularization
Wei Zhuo, Guang Tan
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease
Elisa Gómez de Lope, Saurabh Deshpande, Ramón Viñas Torné, Pietro Liò, Enrico Glaab, Stéphane P. A. Bordas
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen, Yaoxin Wu, Jian Chen, Cong Zhang, Zaharah Bukhsh, Wim Nuijten, Yingqian Zhang
Explainable AI Security: Exploring Robustness of Graph Neural Networks to Adversarial Attacks
Tao Wu, Canyixing Cui, Xingping Xian, Shaojie Qiao, Chao Wang, Lin Yuan, Shui Yu
A Pure Transformer Pretraining Framework on Text-attributed Graphs
Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu
Dr.E Bridges Graphs with Large Language Models through Words
Zipeng Liu, Likang Wu, Ming He, Zhong Guan, Hongke Zhao, Nan Feng
GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks
Fan Zhang, Xin Zhang
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
Tao Wu, Xinwen Cao, Chao Wang, Shaojie Qiao, Xingping Xian, Lin Yuan, Canyixing Cui, Yanbing Liu
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security
Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros