Graph Autoencoders

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12 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Spatially-Aware Self-Organizing Graph Autoencoders via Localized Hebbian Learning
Oct 25, 2026Robin Koç, Hazal Moğultay Özcan, Fatoş T. Yarman VuralGraph Autoencoders Visual Representation Learning
Computer Engineering Department, Middle East Technical University, Ankara, Türkiye
xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs
Oct 22, 2026Daniele Micheletti, Federica Zoe Ricci, Erik B. SudderthGraph Generation Graph Autoencoders
University of California, Irvine · Swarthmore College
KGATE : a Knowledge Graph Embedding Training Environment
Oct 7, 2026Benjamin Loire, Galadriel Brière, Célia Brahimi +2KG Embedding Graph Autoencoders
Aix Marseille Univ., INSERM, Marseille Medical Genetics, Systems Biomedicine Team, Marseille, France · Neurology Therapeutic Area, R&D Servier Paris-Saclay Institut, Gif-sur-Yvette, France · LIRMM, Univ. Montpellier, CNRS, Montpellier, France +1
GeoGAE: Scalable Graph-Level Autoencoding via Hyperball Cloud Representations
Sep 28, 2026Radosław Nowak, Anna Bielawska, Bogusz Stefańczyk +2Graph Neural Networks Graph Representation Learning
Institute of Theoretical and Applied Informatics Polish Academy of Sciences · IDEAS Research Institute · Faculty of Mathematics, Informatics and Mechanics Warsaw University of Technology
Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse
Aug 25, 2026Kleyton da Costa, Bernardo Modenesi, Ivan F. M. Menezes +1Graph Autoencoders Variational Autoencoders
UCL & Holistic AI London, UK · University of Utah Salt Lake City, US · PUC-Rio Rio de Janeiro, Brazil
Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding
Aug 19, 2026Aleksandar Tomčić, Miloš Savić, Miloš RadovanovićGraph Neural Networks Dynamic Graph Learning
Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia Trg Dositeja Obradovi´ca 4, 21000 Novi Sad, Serbia
Unsupervised Graph Representation Learning with Complementary View Alignment
Jul 27, 2026Zengyi Wo, Shiyu Zhang, Qiyao Peng +2Graph Neural Networks Graph Representation Learning
Nanjing University, Nanjing, China · Tianjin University, Tianjin, China
Self-supervised Adversarial Purification for Graph Neural Networks
May 22, 2026Woohyun Lee, Hogun ParkGraph Neural Networks Adversarial Robustness
Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea.
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
May 15, 2026Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2Molecular Representation Learning Graph Transformer
Mohamed bin Zayed University of Artificial Intelligence · LIX, CNRS, École Polytechnique, IP Paris
Graph self-supervised learning based on frequency corruption
Apr 17, 2026Haojie Li, Mengjiao Zhang, Guanfeng Liu +3Graph Neural Networks Graph Representation Learning
School of Data Science, Qingdao University of Science and Technology Qingdao, China · School of Information Science and Technology, Qingdao University of Science and Technology Qingdao, China · School of Computing, Macquarie University Sydney, Australia
From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures
Apr 7, 2026Srinidhi Madabhushi, Pranesh Vyas, Swathi Vaidyanathan +3Graph Autoencoders Anomaly Detection
Amazon Prime Video, USA
Phishing the Phishers with SpecularNet: Hierarchical Graph Autoencoding for Reference-Free Web Phishing Detection
Mar 2, 2026Tailai Song, Pedro Casas, Michela MeoGraph Autoencoders Phishing Detection
Politecnico di Torino Turin, Italy · AIT Austrian Institute of Technology Vienna, Austria

Oct 25, 2026J/K move · Enter open · S save

Spatially-Aware Self-Organizing Graph Autoencoders via Localized Hebbian Learning

Robin Koç, Hazal Moğultay Özcan, Fatoş T. Yarman Vural

Computer Engineering Department, Middle East Technical University, Ankara, Türkiye

Graph Autoencoders Visual Representation Learning Convolutional Autoencoder Graph Structure Learning Graph Neural Networks Hebbian Learning

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Traditional autoencoders rely on static network architectures to learn latent representations. Recently we suggested a dynamic architecture inspired by Hebbian plasticity, enabling topological self-organization among convolutional feature maps (R. Koç et al., Proceedings of the SIU, 2026). Early implementations of this paradigm infer relationships among feature maps using global average pooling and model their interactions through scalar-valued connections. This approach falls short to fully exploit the underlying spatial information, embedded in input images. Furthermore, abrupt transitions between different loss functions and training dynamics can introduce representation shocks, leading to unstable feature learning. In this paper, we introduce a Spatially-Aware Self-Organizing Graph Autoencoder (S-LooM) to address these structural bottlenecks. By using spatial cross-correlation, and spatial kernels, S-LooM establishes dynamic connections among feature maps. We replace static scalar message passing of autoencoders with learnable spatial kernels and eliminate high-frequency deconvolutional artifacts observed in decoders using nearest-neighbor upsampling. Via a soft-blending training curriculum, the latent topology aligns smoothly during training. Our experiments show that S-LooM yields a more stable topology, compared to the state of the art dynamic architectures. Meaning, the graph structure that emerge has more resistance to feature entanglement and noise. It forms functional communities responsible for visual attributes, such as illumination, shape and texture even under high connection densities.