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
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update
Daria Pugacheva, Andrei Ermakov, Igor Lyskov, Ilya Makarov, Yuriy Zotov
Transformer-based Graph Neural Networks for Battery Range Prediction in AIoT Battery-Swap Services
Zhao Li, Yang Liu, Chuan Zhou, Xuanwu Liu, Xuming Pan, Buqing Cao, Xindong Wu
GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
Vipul Gupta, Xin Chen, Ruoyun Huang, Fanlong Meng, Jianjun Chen, Yujun Yan
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation
Jiaxing Zhang, Jiayi Liu, Dongsheng Luo, Jennifer Neville, Hua Wei
Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing
Mounir Ghogho
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme
Jeongmin Brian Park, Kun Wu, Vikram Sharma Mailthody, Zaid Quresh, Scott Mahlke, Wen-mei Hwu
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
Ajay Jaiswal, Nurendra Choudhary, Ravinarayana Adkathimar, Muthu P. Alagappan, Gaurush Hiranandani, Ying Ding, Zhangyang Wang, Edward W Huang, Karthik Subbian
Subgraph Clustering and Atom Learning for Improved Image Classification
Aryan Singh, Pepijn Van de Ven, Ciarán Eising, Patrick Denny
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs
Sumeyye Bas, Kiymet Kaya, Resul Tugay, Sule Gunduz Oguducu
GraphMuse: A Library for Symbolic Music Graph Processing
Emmanouil Karystinaios, Gerhard Widmer
SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids
Salah Ghamizi, Aleksandar Bojchevski, Aoxiang Ma, Jun Cao
Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions
Christian Nauck, Rohan Gorantla, Michael Lindner, Konstantin Schürholt, Antonia S. J. S. Mey, Frank Hellmann
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
Tanvir Hossain, Khaled Mohammed Saifuddin, Muhammad Ifte Khairul Islam, Farhan Tanvir, Esra Akbas
Relaxing Graph Transformers for Adversarial Attacks
Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
Rethinking Fair Graph Neural Networks from Re-balancing
Zhixun Li, Yushun Dong, Qiang Liu, Jeffrey Xu Yu
HyperAggregation: Aggregating over Graph Edges with Hypernetworks
Nicolas Lell, Ansgar Scherp
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Zhenhua Huang, Kunhao Li, Shaojie Wang, Zhaohong Jia, Wentao Zhu, Sharad Mehrotra
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
Zhenhua Huang, Kunhao Li, Shaojie Wang, Zhaohong Jia, Wentao Zhu, Sharad Mehrotra