Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings

Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michail Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:496-504, 2025.

Abstract

Autoencoders based on Graph Neural Networks (GNNs) have garnered significant attention in recent years for their ability to learn informative latent representations of complex topologies, such as graphs. Despite the prevalence of Graph Autoencoders, there has been limited focus on developing and evaluating explainable neural-based graph generative models specifically designed for signed networks. To address this gap, we propose the Signed Graph Archetypal Autoencoder (SGAAE) framework. SGAAE extracts node-level representations that express node memberships over distinct extreme profiles, referred to as archetypes, within the network. This is achieved by projecting the graph onto a learned polytope, which governs its polarization. The framework employs the Skellam distribution for analyzing signed networks combined with relational archetypal analysis and GNNs. Our experimental evaluation demonstrates the SGAAEs’ capability to successfully infer node memberships over underlying latent structures while extracting competing communities. Additionally, we introduce the 2-level network polarization problem and show how SGAAE is able to characterize such a setting. The proposed model achieves high performance in different tasks of signed link prediction across four real-world datasets, outperforming several baseline models. Finally, SGAAE allows for interpretable visualizations in the polytope space, revealing the distinct aspects of the network, as well as, how nodes are expressing them.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-nakis25a, title = {Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings}, author = {Nakis, Nikolaos and Kosma, Chrysoula and Nikolentzos, Giannis and Chatzianastasis, Michail and Evdaimon, Iakovos and Vazirgiannis, Michalis}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {496--504}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/nakis25a/nakis25a.pdf}, url = {https://proceedings.mlr.press/v258/nakis25a.html}, abstract = {Autoencoders based on Graph Neural Networks (GNNs) have garnered significant attention in recent years for their ability to learn informative latent representations of complex topologies, such as graphs. Despite the prevalence of Graph Autoencoders, there has been limited focus on developing and evaluating explainable neural-based graph generative models specifically designed for signed networks. To address this gap, we propose the Signed Graph Archetypal Autoencoder (SGAAE) framework. SGAAE extracts node-level representations that express node memberships over distinct extreme profiles, referred to as archetypes, within the network. This is achieved by projecting the graph onto a learned polytope, which governs its polarization. The framework employs the Skellam distribution for analyzing signed networks combined with relational archetypal analysis and GNNs. Our experimental evaluation demonstrates the SGAAEs’ capability to successfully infer node memberships over underlying latent structures while extracting competing communities. Additionally, we introduce the 2-level network polarization problem and show how SGAAE is able to characterize such a setting. The proposed model achieves high performance in different tasks of signed link prediction across four real-world datasets, outperforming several baseline models. Finally, SGAAE allows for interpretable visualizations in the polytope space, revealing the distinct aspects of the network, as well as, how nodes are expressing them.} }
Endnote
%0 Conference Paper %T Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings %A Nikolaos Nakis %A Chrysoula Kosma %A Giannis Nikolentzos %A Michail Chatzianastasis %A Iakovos Evdaimon %A Michalis Vazirgiannis %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-nakis25a %I PMLR %P 496--504 %U https://proceedings.mlr.press/v258/nakis25a.html %V 258 %X Autoencoders based on Graph Neural Networks (GNNs) have garnered significant attention in recent years for their ability to learn informative latent representations of complex topologies, such as graphs. Despite the prevalence of Graph Autoencoders, there has been limited focus on developing and evaluating explainable neural-based graph generative models specifically designed for signed networks. To address this gap, we propose the Signed Graph Archetypal Autoencoder (SGAAE) framework. SGAAE extracts node-level representations that express node memberships over distinct extreme profiles, referred to as archetypes, within the network. This is achieved by projecting the graph onto a learned polytope, which governs its polarization. The framework employs the Skellam distribution for analyzing signed networks combined with relational archetypal analysis and GNNs. Our experimental evaluation demonstrates the SGAAEs’ capability to successfully infer node memberships over underlying latent structures while extracting competing communities. Additionally, we introduce the 2-level network polarization problem and show how SGAAE is able to characterize such a setting. The proposed model achieves high performance in different tasks of signed link prediction across four real-world datasets, outperforming several baseline models. Finally, SGAAE allows for interpretable visualizations in the polytope space, revealing the distinct aspects of the network, as well as, how nodes are expressing them.
APA
Nakis, N., Kosma, C., Nikolentzos, G., Chatzianastasis, M., Evdaimon, I. & Vazirgiannis, M.. (2025). Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:496-504 Available from https://proceedings.mlr.press/v258/nakis25a.html.

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