Causality-Driven Disentangled Representation Learning in Multiplex Graphs

• URL: http://arxiv.org/abs/2603.24105v1
• Date: Wed, 25 Mar 2026 09:10:28 GMT
• Title: Causality-Driven Disentangled Representation Learning in Multiplex Graphs
• Abstract summary: We introduce a causal inference-based framework that disentangles common and private components in a self-supervised manner.<n>Experiments on synthetic and real-world datasets demonstrate consistent improvements over existing baselines.
• Score: 13.627536649679575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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