Results 291 to 297 of about 2,936,543 (297)

Hypergraph-enhanced Dual Semi-supervised Graph Classification

International Conference on Machine Learning
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs.
Wei Ju, Zhengyan Mao, Siyu Yi, Yifang Qin, Yiyang Gu, Zhiping Xiao, Yifan Wang, Xiao Luo, Ming Zhang   +8 more
semanticscholar   +1 more source

GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

Science China Information Sciences
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
Wei Ju, Yiyang Gu, Zhengyan Mao, Ziyue Qiao, Yifang Qin, Xiao Luo, Hui Xiong, Ming Zhang   +7 more
semanticscholar   +1 more source

Make Heterophilic Graphs Better Fit GNN: A Graph Rewiring Approach

IEEE Transactions on Knowledge and Data Engineering
Graph Neural Networks (GNNs) have shown superior performance in modeling graph data. Existing studies have shown that a lot of GNNs perform well on homophilic graphs while performing poorly on heterophilic graphs.
Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang, Shi Han, Dongmei Zhang   +5 more
semanticscholar   +1 more source

Generative Essential Graph Convolutional Network for Multi-View Semi-Supervised Classification

IEEE transactions on multimedia
Multi-view learning is a promising research field that aims to enhance learning performance by integrating information from diverse data perspectives.
Jielong Lu, Zhihao Wu, Luying Zhong, Zhaoliang Chen, Hong Zhao, Shiping Wang   +5 more
semanticscholar   +1 more source

Graph(Graph): A Nested Graph-Based Framework for Early Accident Anticipation

IEEE Workshop/Winter Conference on Applications of Computer Vision
Anticipating traffic accidents early using dashcam videos is an important task for ensuring road safety and building reliable intelligent autonomous vehicles.
Nupur Thakur, PrasanthSai Gouripeddi, Baoxin Li   +2 more
semanticscholar   +1 more source

GALA: Graph Diffusion-Based Alignment With Jigsaw for Source-Free Domain Adaptation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy.
Junyu Luo, Yiyang Gu, Xiao Luo, Wei Ju, Zhiping Xiao, Yusheng Zhao, Jingyang Yuan, Ming Zhang   +7 more
semanticscholar   +1 more source

A novel graph oversampling framework for node classification in class-imbalanced graphs

Science China Information Sciences
Riting Xia, Chunxu Zhang, Yan Zhang, Xueyan Liu, Bo Yang   +4 more
semanticscholar   +1 more source