3 papers
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.LG2024
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization
Shixuan Liu, Yanghe Feng, Keyu Wu +3
In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle with unoriented edges or latent assumpti…
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…