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arXiv · 2603.24105

Causality-Driven Disentangled Representation Learning in Multiplex Graphs

Saba Nasiri·Selin Aviyente·Dorina Thanou
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Abstract

Learning representations from multiplex graphs, i.e., multi-layer networks where nodes interact through multiple relation types, is challenging due to the entanglement of shared (common) and layer-specific (private) information, which limits generalization and interpretability. In this work, we introduce a causal inference-based framework that disentangles common and private components in a self-supervised manner. CaDeM jointly (i) aligns shared embeddings across layers, (ii) enforces private embeddings to capture layer-specific signals, and (iii) applies backdoor adjustment to ensure that the common embeddings capture only global information while being separated from the private representations. Experiments on synthetic and real-world datasets demonstrate consistent improvements over existing baselines, highlighting the effectiveness of our approach for robust and interpretable multiplex graph representation learning.

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Saba Nasiri, Selin Aviyente, Dorina Thanou. 2026-03-25. Causality-Driven Disentangled Representation Learning in Multiplex Graphs. https://arxiv.org/abs/2603.24105

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