Special Session of the 34th International Conference on Artificial Neural Networks  (ICANN 2025)

September 9-12, 2025, Kaunas, Lithuania

Call for papers

Graphs play a crucial role in different fields in modeling complex structures composed of entities and their relationships, including dynamic domains where these relationships can evolve over time. Notable examples of graph-based representations and processing can be found in biology, where the structures of molecules and proteins are naturally modeled as graphs; social sciences, where graphs are used to model interactions between individuals or groups; data science, where graphs enhance recommendation systems by tracking user-item interactions; and transportation, where graphs are employed to model the evolution of traffic flow over time.

Neural models on graphs enable adaptive solutions for a wide range of learning tasks on graph data, avoiding the need for hand-engineered features or domain-specific knowledge. This capability has driven significant progress in applying machine learning to graph-based problems across various research fields. As a result, the design, optimization, and analysis of these graph-based learning models have become central to cutting-edge research, while also presenting a range of open challenges that continue to shape the field's future directions.

This special session at ICANN 2025 aims to bring together cutting-edge research and new ideas in neural networks and machine learning models for graphs. We encourage the submission of works that address open challenges by advancing both theoretical investigations and practical applications.

Topics

Topics of interest to this session include, but are not limited to:

• Graph neural networks based on convolutional, recurrent, and transformer architectures

• Temporal and dynamic graphs

• Relational inference, heterogeneous graphs

• Graph pooling, graph structure learning

• Open problems in representation learning, e.g. over-smoothing, over-squashing, heterophily

• Graph learning for time series, including data imputation

• Theory of graph learning

• Graph signal processing, including spectral methods for analysis and design

• Trustworthy AI for graph learning, including explainability (XAI), robustness, reliability

• Graph-based methodologies for pattern recognition

• Other methods for learning on graphs, including kernel-based approaches

• Datasets and benchmarks for learning on graphs

• Applications of graph learning, including:

  • Chemistry and biology, e.g. toxicology, protein interactions

  • Graph learning on brain data

  • Social sciences, social networks

  • Graph learning for ecology

  • Knowledge engineering and discovery

  • Sensor networks and IoT applications

  • … and many more!

Important dates

Deadline for full paper and extended abstract submission: 1 May 2025 15 May 2025 (final extension)

Notification of acceptance: 1 June 2025 15 June 2025

Camera-ready upload: 15 June 2025 25 June 2025

Conference dates: 9-12 September 2025

Submission instructions

The special session contributions will be published as part of the ICANN 2025 proceedings in the Springer Lecture Notes in Computer Science (LNCS) series, indexed as a peer-reviewed publication in the Web of Science.

Submit your contribution using the instructions provided at https://e-nns.org/icann2025/submission on the conference management system. Select the “Special Session on Neural Networks for Graphs and Beyond” track on Microsoft CMT.

We follow a double-blind review process.

Call for reviewers

Please volunteer as a reviewer to help us ensure the quality of the papers presented at this special session. Apply here.

Organizers

Alessio Micheli, University of Pisa, Italy

Benoit Gaüzère, INSA Rouen Normandie, France

Domenico Tortorella, University of Pisa, Italy

Filippo Maria Bianchi, UiT the Arctic University of Norway

Luca Pasa, University of Padova, Italy

Nicolò Navarin, University of Padova, Italy

Vincenzo Carletti, University of Salerno, Italy

Special session organized with the contribution of the Project DEEP-GRAPH, funded by the Italian Ministry of University and Research, PRIN 2022 (project code: 2022YLRBTT, CUP: I53C24002440006).