3 papers
cs.LG2026
Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
Qinhan Hou, Jing Tang
Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adap…
cs.LG2026
Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
Qinhan Hou, Jing Tang
Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while others are better served by loca…
cs.LG2025
Exploring Heterophily in Graph-level Tasks
Qinhan Hou, Yilun Zheng, Xichun Zhang +2
While heterophily has been widely studied in node-level tasks, its impact on graph-level tasks remains unclear. We present the first analysis of heterophily in graph-level learning…