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
Graph Neural Networks in Histopathology: Emerging Trends and Future Directions
Siemen Brussee, Giorgio Buzzanca, Anne M. R. Schrader, Jesper Kers
Research and Implementation of Data Enhancement Techniques for Graph Neural Networks
Jingzhao Gu, Haoyang Huang
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang
A data-centric approach for assessing progress of Graph Neural Networks
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla
Semantic Graph Consistency: Going Beyond Patches for Regularizing Self-Supervised Vision Transformers
Chaitanya Devaguptapu, Sumukh Aithal, Shrinivas Ramasubramanian, Moyuru Yamada, Manohar Kaul
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
Emily Shinkle, Aleksandra Pachalieva, Riti Bahl, Sakib Matin, Brendan Gifford, Galen T. Craven, Nicholas Lubbers
A Scalable and Effective Alternative to Graph Transformers
Kaan Sancak, Zhigang Hua, Jin Fang, Yan Xie, Andrey Malevich, Bo Long, Muhammed Fatih Balin, Ümit V. Çatalyürek
When Box Meets Graph Neural Network in Tag-aware Recommendation
Fake Lin, Ziwei Zhao, Xi Zhu, Da Zhang, Shitian Shen, Xueying Li, Tong Xu, Suojuan Zhang, Enhong Chen
Scalable Expressiveness through Preprocessed Graph Perturbations
Danial Saber, Amirali Salehi-Abari
Graph Knowledge Distillation to Mixture of Experts
Pavel Rumiantsev, Mark Coates
Graph Neural Thompson Sampling
Shuang Wu, Arash A. Amini
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
Xiao Yang, Gaolei Li, Jianhua Li
Geodesic Distance Between Graphs: A Spectral Metric for Assessing the Stability of Graph Neural Networks
Soumen Sikder Shuvo, Ali Aghdaei, Zhuo Feng
A Unified Graph Selective Prompt Learning for Graph Neural Networks
Bo Jiang, Hao Wu, Ziyan Zhang, Beibei Wang, Jin Tang
Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Networks
Zhiwei Zhang, Minhua Lin, Junjie Xu, Zongyu Wu, Enyan Dai, Suhang Wang
Benchmarking Spectral Graph Neural Networks: A Comprehensive Study on Effectiveness and Efficiency
Ningyi Liao, Haoyu Liu, Zulun Zhu, Siqiang Luo, Laks V. S. Lakshmanan