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
Novel Representation Learning Technique using Graphs for Performance Analytics
Tarek Ramadan, Ankur Lahiry, Tanzima Z. Islam
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
Hao Qian, Hongting Zhou, Qian Zhao, Hao Chen, Hongxiang Yao, Jingwei Wang, Ziqi Liu, Fei Yu, Zhiqiang Zhang, Jun Zhou
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
Fali Wang, Tianxiang Zhao, Suhang Wang
Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
Gang Qu, Anton Orlichenko, Junqi Wang, Gemeng Zhang, Li Xiao, Aiying Zhang, Zhengming Ding, Yu-Ping Wang
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Yutong Xia, Runpeng Yu, Yuxuan Liang, Xavier Bresson, Xinchao Wang, Roger Zimmermann
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
Ruizhe Zhang, Xinke Jiang, Yuchen Fang, Jiayuan Luo, Yongxin Xu, Yichen Zhu, Xu Chu, Junfeng Zhao, Yasha Wang
On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks
Walid Guettala, László Gulyás
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting
Qipeng Qian, Tanwi Mallick
Use of Graph Neural Networks in Aiding Defensive Cyber Operations
Shaswata Mitra, Trisha Chakraborty, Subash Neupane, Aritran Piplai, Sudip Mittal
Graph Q-Learning for Combinatorial Optimization
Victoria M. Dax, Jiachen Li, Kevin Leahy, Mykel J. Kochenderfer
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups
Anna Stephens, Francisco Santos, Pang-Ning Tan, Abdol-Hossein Esfahanian
Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networks
Damiano Zanardini, Emilio Serrano