Causality-Driven Disentangled Representation Learning in Multiplex Graphs
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917361130405888 |
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| author | Nasiri, Saba Aviyente, Selin Thanou, Dorina |
| author_facet | Nasiri, Saba Aviyente, Selin Thanou, Dorina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24105 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causality-Driven Disentangled Representation Learning in Multiplex Graphs Nasiri, Saba Aviyente, Selin Thanou, Dorina Machine Learning Social and Information Networks 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. |
| title | Causality-Driven Disentangled Representation Learning in Multiplex Graphs |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2603.24105 |