Causal graphs are powerful tools for understanding cause-effect relationships in dynamic systems [1], enabling effective intervention strategies in complex and evolving scenarios. The Dynamic Causal ...

To address this issue, we build the new paradigm of KGE in the context of causality and embedding disentanglement. We further propose a Causality-enhanced knowledge graph Embedding (CausE) framework.

In this paper, we propose a Memory-Aware Graph Interactive Causal Network (MagicNet) that considers both temporal and spatial dependencies in financial documents and introduces causality-based ...