3 papers
cs.LG2026
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
Bo Liu, Di Dai, Jingwei Liu +5
Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems. However, existing neural GCD methods predominantly rely on a "one-size-fits-al…
cs.LG2026
SPGCL: Simple yet Powerful Graph Contrastive Learning via SVD-Guided Structural Perturbation
Hao Deng, Zhang Guo, Shuiping Gou +1
Graph Neural Networks (GNNs) are sensitive to structural noise from adversarial attacks or imperfections. Existing graph contrastive learning (GCL) methods typically rely on either…
cs.LG2026
GADPN: Graph Adaptive Denoising and Perturbation Networks via Singular Value Decomposition
Hao Deng, Bo Liu
While Graph Neural Networks (GNNs) excel on graph-structured data, their performance is fundamentally limited by the quality of the observed graph, which often contains noise, miss…