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
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang, Siwei Wang, Yu Feng, Yu Shi, Ruicaho Ren, En Zhu, Xinwang Liu
Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces
Yue Jiang, Changkong Zhou, Vikas Garg, Antti Oulasvirta
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
Qixuan Zhang, Zhifeng Wang, Yang Liu, Zhenyue Qin, Kaihao Zhang, Sabrina Caldwell, Tom Gedeon
Graph Neural Networks for Protein-Protein Interactions -- A Short Survey
Mingda Xu, Peisheng Qian, Ziyuan Zhao, Zeng Zeng, Jianguo Chen, Weide Liu, Xulei Yang
Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks
Joshua Melton, Shannon Reid, Gabriel Terejanu, Siddharth Krishnan
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection
Abdeljalil Zoubir, Badr Missaoui
Solving the Tree Containment Problem Using Graph Neural Networks
Arkadiy Dushatskiy, Esther Julien, Leen Stougie, Leo van Iersel
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Tong Qiao, Jianlei Yang, Yingjie Qi, Ao Zhou, Chen Bai, Bei Yu, Weisheng Zhao, Chunming Hu
Late Breaking Results: Fast System Technology Co-Optimization Framework for Emerging Technology Based on Graph Neural Networks
Tianliang Ma, Guangxi Fan, Xuguang Sun, Zhihui Deng, Kainlu Low, Leilai Shao
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism
Shuzhou Yuan, Michael Färber
Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach
Alireza Rashnu, Armin Salimi-Badr
Fair Graph Neural Network with Supervised Contrastive Regularization
Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes