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
SafePowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models
Fabien Bernier, Jun Cao, Maxime Cordy, Salah Ghamizi
Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
Vinay Sharma, Olga Fink
Generalizable Graph Neural Networks for Robust Power Grid Topology Control
Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova
DeltaGNN: Graph Neural Network with Information Flow Control
Kevin Mancini, Islem Rekik
Automated Heterogeneous Network learning with Non-Recursive Message Passing
Zhaoqing Li, Maiqi Jiang, Shengyuan Chen, Bo Li, Guorong Chen, Xiao Huang
Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization
Pablo Loyola, Kento Hasegawa, Andres Hoyos-Idobro, Kazuo Ono, Toyotaro Suzumura, Yu Hirate, Masanao Yamaoka
Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
Jiale Zhang, Bosen Rao, Chengcheng Zhu, Xiaobing Sun, Qingming Li, Haibo Hu, Xiapu Luo, Qingqing Ye, Shouling Ji
LightGNN: Simple Graph Neural Network for Recommendation
Guoxuan Chen, Lianghao Xia, Chao Huang
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael Bronstein, Haggai Maron, Fabrizio Frasca
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks
Atmane Ayoub Mansour Bahara, Kamel Soaïd Ferrahia, Mohamed-Lamine Messai, Hamida Seba, Karima Amrouche
A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detection
Batool Lakzaei, Mostafa Haghir Chehreghani, Alireza Bagheri
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
Ting Wang, Zhixin Zhou, Rui Luo