[2603.24105] Causality-Driven Disentangled Representation Learning in Multiplex Graphs

Computer Science > Machine Learning arXiv:2603.24105 (cs) [Submitted on 25 Mar 2026] Title:Causality-Driven Disentangled Representation Learning in Multiplex Graphs Authors:Saba Nasiri, Selin Aviyente, Dorina Thanou View a PDF of the paper titled Causality-Driven Disentangled Representation Learning in Multiplex Graphs, by Saba Nasiri and 2 other authors View PDF HTML (experimental) 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. Comments: Subjects: Machine Learning (cs.LG); Social and Information Networks (cs.SI) Cite as: arXiv:2603.24105 [cs.LG]   (or arXiv:2603.24105v1 [cs.LG] for this versio...