Neural Networks for Graphs and Beyond (NN4G+) @ ICANN 2025

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. 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.

Important dates

Deadline for full paper and extended abstract submission: 1 May 2025
Notification of acceptance: 1 June 2025
Camera-ready upload: 15 June 2025
Conference dates: 9-12 September 2025

Description

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.

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!

Submission instructions

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