45 citations · 83 across the 43 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
stat.ML2025
Graph Distribution-valued Signals: A Wasserstein Space Perspective
Yanan Zhao, Feng Ji, Xingchao Jian +1
We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs), which are probability distributions in the Wassers…
stat.ML2021★ 4 cited
Learning on heterogeneous graphs using high-order relations
See Hian Lee, Feng Ji, Wee Peng Tay
A heterogeneous graph consists of different vertices and edges types. Learning on heterogeneous graphs typically employs meta-paths to deal with the heterogeneity by reducing the g…