Assistant Professor, New York University Shanghai
2 papers at NeurIPS 2025
We identify a smoothness-generalization dilemma in message passing that limits GNN universality across varying homophily and propose the Inceptive Graph Neural Network (IGNN), a universal framework to address the dilemma.
We propose the first comprehensive and unified benchmark for deep graph clustering, offer an open-source package named PyDGC, and point out promising research directions for DGC from extensive experimental analyses.
