Temporal Graph Embedding with Causality-Aware Propagation

Abstract

Temporal graph embeddings aim to capture the evolving behavior of graphs over time, a crucial task in domains like social network analysis, knowledge graph reasoning, and anomaly detection. However, current graph embedding techniques often treat temporal relationships as simple sequential adjacency updates, neglecting the underlying causal structure that governs how nodes influence each other across time. This paper introduces a novel approach – Causality-Aware Propagation (CAP) – that explicitly models causal relationships within evolving graphs to generate more accurate and informative embeddings. CAP leverages event sequences and domain knowledge to define a causal graph, then employs a modified diffusion process where node representations are propagated based on the learned strength of these causal links. The core idea is to move beyond mere connection propagation to represent the *influence* of connections over time. We demonstrate the effectiveness of CAP through a theoretical analysis and explore its potential applications, establishing a foundational technique for temporal graph representation learning. The method's key contribution lies in its integration of causal inference with graph embedding, offering a more robust and interpretable representation of dynamic graph structures.