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
Logical Characterizations of Recurrent Graph Neural Networks with Reals and Floats
Veeti Ahvonen, Damian Heiman, Antti Kuusisto, Carsten Lutz
Explaining Graph Neural Networks via Structure-aware Interaction Index
Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation
Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang
Similarity-Navigated Conformal Prediction for Graph Neural Networks
Jianqing Song, Jianguo Huang, Wenyu Jiang, Baoming Zhang, Shuangjie Li, Chongjun Wang
Graph Sparsification via Mixture of Graphs
Guibin Zhang, Xiangguo Sun, Yanwei Yue, Chonghe Jiang, Kun Wang, Tianlong Chen, Shirui Pan
Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks
Yafeng Yan, Shuyao He, Zhou Yu, Jiajie Yuan, Ziang Liu, Yan Chen
Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks
Dominik Köhler, Stefan Heindorf
Unleash Graph Neural Networks from Heavy Tuning
Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
Zhaoning Yu, Hongyang Gao
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, Hui-Ling Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan
Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
Zhiwei Zhang, Minhua Lin, Enyan Dai, Suhang Wang
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
Rongrong Ma, Guansong Pang, Ling Chen
Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation
Guojun Liang, Prayag Tiwari, Slawomir Nowaczyk, Stefan Byttner
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
Zhehan Zhao, Lu Bai, Lixin Cui, Ming Li, Yue Wang, Lixiang Xu, Edwin R. Hancock