6 papers
Graph Transformers: A Survey
Ahsan Shehzad, Feng Xia, Shagufta Abid +4
Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph…
Brain PathoGraph Learning
Ciyuan Peng, Nguyen Linh Dan Le, Shan Jin +3
Brain graph learning has demonstrated significant achievements in the fields of neuroscience and artificial intelligence. However, existing methods struggle to selectively learn di…
Factor Graph-based Interpretable Neural Networks
Yicong Li, Kuanjiu Zhou, Shuo Yu +4
Comprehensible neural network explanations are foundations for a better understanding of decisions, especially when the input data are infused with malicious perturbations. Existin…
Biologically Plausible Brain Graph Transformer
Ciyuan Peng, Yuelong Huang, Qichao Dong +4
State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and th…
Long-range Brain Graph Transformer
Shuo Yu, Shan Jin, Ming Li +2
Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilita…
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
Shuo Yu, Yingbo Wang, Ruolin Li +7
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve…