[ICRA 2025] We present a Morphology-Informed Heterogeneous Graph Neural Network (MI-HGNN) for learning-based contact perception. The architecture and connectivity of the MI-HGNN are constructed from the robot morphology.
Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding (ICLR-2026)
ReHGNN is a deep learning framework for intelligent server recommendation in distributed large-model deployment scenarios.
Dynamically discovering metapaths, required for processing in various Heterogeneous graph neural network models. Eliminating the need of manual metapath providing. Ranking metapaths based on the downstream task to get the best metapaths intended towards the downstream task.
Applied Hypergraph Neural Networks: An Implementation and Study on The Effects of Homophily and Construction Techniques
Infra2Graph prototype for bridge impact assessment in city scale. The system integrates heterogeneous graph analysis and HGNN-based closure-impact prediction, with Exp-4-lite (kNN, no edge attributes, weak quantile-based weighting) adopted as the reference MVP configuration.
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