Connectionists: [CfP] Special session on Neural Networks for Graphs and Beyond (NN4G+) @ ICANN 2026 - Final Extension

[Davide Rigoni]

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CALL FOR PAPERS (ICANN 2026)
Special Session on Neural Networks for Graphs and Beyond (NN4G+)

https://e-nns.org/icann2026/neural-networks-for-graphs-and-beyond-nn4g-2026/

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This special session at ICANN 2026 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
2026 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 submission: 30 March 2026 (final extension)
-> Notification of acceptance: 29 May 2026
-> Camera-ready upload: 29 June 2026
-> Author registration and early registration at early rate: 29 June 2026
-> Conference dates: 14-17 September 2026


****** 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, and 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/icann2026/submission on the conference management system.
Select the “Special Session on Neural Networks for Graphs and Beyond” track
as the Primary Subject Area on Microsoft CMT.
We follow a double-blind review process.
At least one author must register and attend the conference in person.

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

https://forms.gle/afzkAY4QAUoXZmpD6

****** Session Organisers ******
-> Assandro Sperduti, University of Padova, Italy
-> Benoit Gaüzère, INSA Rouen Normandie, France
-> Caterina Graziani, University of Siena, Italy
-> Davide Rigoni, University of Padova, Italy
-> Domenico Tortorella, University of Pisa, Italy
-> Filippo Maria Bianchi, UiT the Arctic University of Norway
-> Matteo Tolloso, University of Pisa, Italy
-> Sara Bacconi, University of Siena, Italy
-> Vincenzo Carletti, University of Salerno, Italy
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