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
Hierarchical Compression of Text-Rich Graphs via Large Language Models
Shichang Zhang, Da Zheng, Jiani Zhang, Qi Zhu, Xiang song, Soji Adeshina, Christos Faloutsos, George Karypis, Yizhou Sun
Trajectory Planning for Autonomous Driving in Unstructured Scenarios Based on Graph Neural Network and Numerical Optimization
Sumin Zhang, Kuo Li, Rui He, Zhiwei Meng, Yupeng Chang, Xiaosong Jin, Ri Bai
Introducing Diminutive Causal Structure into Graph Representation Learning
Hang Gao, Peng Qiao, Yifan Jin, Fengge Wu, Jiangmeng Li, Changwen Zheng
Conformal Load Prediction with Transductive Graph Autoencoders
Rui Luo, Nicolo Colombo
Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction
Juzheng Zhang, Lanning Wei, Zhen Xu, Quanming Yao
How Interpretable Are Interpretable Graph Neural Networks?
Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes
Asim Waqas, Aakash Tripathi, Paul Stewart, Mia Naeini, Ghulam Rasool
Logical Distillation of Graph Neural Networks
Alexander Pluska, Pascal Welke, Thomas Gärtner, Sagar Malhotra
On the Hölder Stability of Multiset and Graph Neural Networks
Yair Davidson, Nadav Dym
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training
Xizhi Gu, Hongzheng Li, Shihong Gao, Xinyan Zhang, Lei Chen, Yingxia Shao
Graph Mining under Data scarcity
Appan Rakaraddi, Lam Siew-Kei, Mahardhika Pratama, Marcus de Carvalho
GENIE: Watermarking Graph Neural Networks for Link Prediction
Venkata Sai Pranav Bachina, Ankit Gangwal, Aaryan Ajay Sharma, Charu Sharma
Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network
Shirwan Piroti, Ashima Chawla, Tahar Zanouda
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten