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
Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors
Jinhyeok Choi, Heehyeon Kim, Joyce Jiyoung Whang
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Rong Shan, Jianghao Lin, Chenxu Zhu, Bo Chen, Menghui Zhu, Kangning Zhang, Jieming Zhu, Ruiming Tang, Yong Yu, Weinan Zhang
GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network
Xianfeng Song, Yi Zou, Zheng Shi, Zheng Liu
Exact Acceleration of Subgraph Graph Neural Networks by Eliminating Computation Redundancy
Qian Tao, Xiyuan Wang, Muhan Zhang, Shuxian Hu, Wenyuan Yu, Jingren Zhou
Graph Neural Networks Are Evolutionary Algorithms
Kaichen Ouyang, Shengwei Fu
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
Fabrizio Frasca, Fabian Jogl, Moshe Eliasof, Matan Ostrovsky, Carola-Bibiane Schönlieb, Thomas Gärtner, Haggai Maron
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
Jiawen Qin, Pengfeng Huang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Jianxin Li
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning
Jaesun Shin, Eunjoo Jeon, Taewon Cho, Namkyeong Cho, Youngjune Gwon
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Song Wang, Zhenyu Lei, Zhen Tan, Jiaqi Ding, Xinyu Zhao, Yushun Dong, Guorong Wu, Tianlong Chen, Chen Chen, Aiying Zhang, Jundong Li
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
Yilong Zang, Lingfei Ren, Yue Li, Zhikang Wang, David Antony Selby, Zheng Wang, Sebastian Josef Vollmer, Hongzhi Yin, Jiangning Song, Junhang Wu
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
Hossein Rafiee Zade, Hadi Zare, Mohsen Ghassemi Parsa, Hadi Davardoust, Meshkat Shariat Bagheri
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
Erhu He, Declan Kutscher, Yiqun Xie, Jacob Zwart, Zhe Jiang, Huaxiu Yao, Xiaowei Jia
Effective Context Modeling Framework for Emotion Recognition in Conversations
Cuong Tran Van, Thanh V. T. Tran, Van Nguyen, Truong Son Hy
Learning Cross-Task Generalities Across Graphs via Task-trees
Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
THeGCN: Temporal Heterophilic Graph Convolutional Network
Yuchen Yan, Yuzhong Chen, Huiyuan Chen, Xiaoting Li, Zhe Xu, Zhichen Zeng, Zhining Liu, Hanghang Tong
Prompt-based Unifying Inference Attack on Graph Neural Networks
Yuecen Wei, Xingcheng Fu, Lingyun Liu, Qingyun Sun, Hao Peng, Chunming Hu
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
Van Thuy Hoang, O-Joun Lee
Graph Structure Refinement with Energy-based Contrastive Learning
Xianlin Zeng, Yufeng Wang, Yuqi Sun, Guodong Guo, Baochang Zhang, Wenrui Ding