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
Generating Robust Counterfactual Witnesses for Graph Neural Networks
Dazhuo Qiu, Mengying Wang, Arijit Khan, Yinghui Wu
EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision
Yufeng Yang, Adrian Kneip, Charlotte Frenkel
Training-free Graph Neural Networks and the Power of Labels as Features
Ryoma Sato
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
Yi Li, Renyou Xie, Chaojie Li, Yi Wang, Zhaoyang Dong
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Duna Zhan, Dongliang Guo, Pengsheng Ji, Sheng Li
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang Song, Wee Peng Tay
Power Failure Cascade Prediction using Graph Neural Networks
Sathwik Chadaga, Xinyu Wu, Eytan Modiano
A General Black-box Adversarial Attack on Graph-based Fake News Detectors
Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian, Keke Tang, Zhen Wang
Gradformer: Graph Transformer with Exponential Decay
Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Shirui Pan, Wenbin Hu
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
Zhaoyang Chu, Yao Wan, Qian Li, Yang Wu, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search
Haoming Zhang, Ran Cheng
NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator
Kaustubh Shivdikar, Nicolas Bohm Agostini, Malith Jayaweera, Gilbert Jonatan, Jose L. Abellan, Ajay Joshi, John Kim, David Kaeli
Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
Antonios Makris, Theodoros Theodoropoulos, Evangelos Psomakelis, Emanuele Carlini, Matteo Mordacchini, Patrizio Dazzi, Konstantinos Tserpes
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks
Zhe Zhao, Pengkun Wang, Xu Wang, Haibin Wen, Xiaolong Xie, Zhengyang Zhou, Qingfu Zhang, Yang Wang
Graph Machine Learning in the Era of Large Language Models (LLMs)
Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li