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
Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
Levi Rauchwerger, Stefanie Jegelka, Ron Levie
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, Michalis Vazirgiannis
Exploiting the Structure of Two Graphs with Graph Neural Networks
Victor M. Tenorio, Antonio G. Marques
Centrality Graph Shift Operators for Graph Neural Networks
Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, Michalis Vazirgiannis
Cybercrime Prediction via Geographically Weighted Learning
Muhammad Al-Zafar Khan, Jamal Al-Karaki, Emad Mahafzah
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Jhony H. Giraldo, Aref Einizade, Andjela Todorovic, Jhon A. Castro-Correa, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros
Financial Fraud Detection using Jump-Attentive Graph Neural Networks
Prashank Kadam
ComFairGNN: Community Fair Graph Neural Network
Yonas Sium, Qi Li
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
Shuangjie Li, Jiangqing Song, Baoming Zhang, Gaoli Ruan, Junyuan Xie, Chongjun Wang
Graph neural networks and non-commuting operators
Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar
Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction
Zesheng Liu, Maryam Rahnemoonfar
Reconsidering the Performance of GAE in Link Prediction
Weishuo Ma, Yanbo Wang, Xiyuan Wang, Muhan Zhang
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems
Chenchuhui Hu
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Shuangjie Li, Baoming Zhang, Jianqing Song, Gaoli Ruan, Chongjun Wang, Junyuan Xie
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
Hanyang Yuan, Jiarong Xu, Renhong Huang, Mingli Song, Chunping Wang, Yang Yang
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Shan Zhao, Zhaiyu Chen, Zhitong Xiong, Yilei Shi, Sudipan Saha, Xiao Xiang Zhu
DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
Zelin Yao, Chuang Liu, Xianke Meng, Yibing Zhan, Jia Wu, Shirui Pan, Wenbin Hu
DM4Steal: Diffusion Model For Link Stealing Attack On Graph Neural Networks
Jinyin Chen, Haonan Ma, Haibin Zheng
JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
Wanying Ding, Manoj Cherukumalli, Santosh Chikoti, Vinay K. Chaudhri