3 papers
cond-mat.dis-nn2026
Optimizing p-spin models through hypergraph neural networks and deep reinforcement learning
Li Zeng, Mutian Shen, Tianle Pu +5
p-spin glasses, characterized by frustrated many-body interactions beyond the conventional pairwise case (p>2), are prototypical disordered systems whose ground-state search is NP-…
cs.LG2025
The Expressive Power of Graph Neural Networks: A Survey
Bingxu Zhang, Changjun Fan, Shixuan Liu +4
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoreti…
cs.AI2024
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Shixuan Liu, Changjun Fan, Kewei Cheng +4
Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation t…