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
Graphs Unveiled: Graph Neural Networks and Graph Generation
László Kovács, Ali Jlidi
Dual-Channel Multiplex Graph Neural Networks for Recommendation
Xiang Li, Chaofan Fu, Zhongying Zhao, Guanjie Zheng, Chao Huang, Junyu Dong, Yanwei Yu
Graph Partial Label Learning with Potential Cause Discovering
Hang Gao, Jiaguo Yuan, Jiangmeng Li, Peng Qiao, Fengge Wu, Changwen Zheng, Huaping Liu
Layer-diverse Negative Sampling for Graph Neural Networks
Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan
DynamicGlue: Epipolar and Time-Informed Data Association in Dynamic Environments using Graph Neural Networks
Theresa Huber, Simon Schaefer, Stefan Leutenegger
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction
Asma Sattar, Georgios Deligiorgis, Marco Trincavelli, Davide Bacciu
Graph Expansion in Pruned Recurrent Neural Network Layers Preserve Performance
Suryam Arnav Kalra, Arindam Biswas, Pabitra Mitra, Biswajit Basu
Forward Learning of Graph Neural Networks
Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation
Xiaoyu Li, Wenwen Min, Shunfang Wang, Changmiao Wang, Taosheng Xu
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
Rui Zhang, Dawei Cheng, Xin Liu, Jie Yang, Yi Ouyang, Xian Wu, Yefeng Zheng
Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
Mohammad J. Aljubran, Roland N. Horne
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
Zhaoliang Chen, Zhihao Wu, Ylli Sadikaj, Claudia Plant, Hong-Ning Dai, Shiping Wang, Wenzhong Guo
Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs
Jie Liu, Xuequn Shang, Xiaolin Han, Wentao Zhang, Hongzhi Yin
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
Raffaele Marino, Lorenzo Buffoni, Bogdan Zavalnij
Link Prediction for Social Networks using Representation Learning and Heuristic-based Features
Samarth Khanna, Sree Bhattacharyya, Sudipto Ghosh, Kushagra Agarwal, Asit Kumar Das
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Tobias Hille, Maximilian Stubbemann, Tom Hanika
Causal Graph Neural Networks for Wildfire Danger Prediction
Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong, Ioannis Papoutsis, Gustau Camps-Valls, Xiao Xiang Zhu
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations
Harish G. Naik, Jan Polster, Raj Shekhar, Tamás Horváth, György Turán
Towards Graph Foundation Models for Personalization
Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
Keke Huang, Wencai Cao, Hoang Ta, Xiaokui Xiao, Pietro Liò