38 citations · 70 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
Graph Learning
Feng Xia, Ciyuan Peng, Jing Ren +5
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining…
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…
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…
Learning on Multimodal Graphs: A Survey
Ciyuan Peng, Jiayuan He, Feng Xia
Multimodal data pervades various domains, including healthcare, social media, and transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal graphs…
Quantum Graph Learning: Frontiers and Outlook
Shuo Yu, Ciyuan Peng, Yingbo Wang +3
Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates t…
Physics-Informed Graph Learning
Ciyuan Peng, Feng Xia, Vidya Saikrishna +1
An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and…