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

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Efficient End-to-end Language Model Fine-tuning on Graphs
Rui Xue, Xipeng Shen, Ruozhou Yu, Xiaorui Liu
Large Language Model Graph Neural Network Fine Tuning Graph Drawing Semi Supervised Learning Text Attributed Graph Efficient Large Language Model

Graph Neural Network

Papers

Dynamic Spiking Graph Neural Networks

Rethinking Causal Relationships Learning in Graph Neural Networks

COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems

Graph Neural Networks with Diverse Spectral Filtering

Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection

Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning

Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications

GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks

I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,

Robust Graph Neural Network based on Graph Denoising

IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI

Detecting Contextual Network Anomalies with Graph Neural Networks

No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

Factorized Explainer for Graph Neural Networks

Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks

Synthesizing Traffic Datasets using Graph Neural Networks

Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks

Efficient End-to-end Language Model Fine-tuning on Graphs