Deep Learning for Graphs is a special session at the 2023 International Joint Conference on Neural Networks, which will be held in Gold Coast, Queensland, Australia on June 18-23, 2023. 

Call for papers

The research field of deep learning for graphs studies the application of well-known deep learning concepts, such as convolution operators on images, to the processing of graph-structured data. Graphs are abstract objects that naturally represent interacting systems of entities, where interactions denote functional and/or structural dependencies between them. 

Molecular compounds and social networks are the most common examples of such graphs: on the one hand, a molecule is seen as a system of interacting atoms, whose bonds depend, e.g., on their inter-atomic distance; on the other hand, a social network represents a vastly heterogeneous set of user-user interactions, as well as between users and items, like, pictures, movies and songs. Besides, graph representations are extremely useful in far more domains, for instance to encode symmetries and constraints of combinatorial optimization problems as a proxy of our a-priori knowledge. For these reasons, learning how to properly map graphs and their nodes to values of interest poses extremely important, yet challenging, research questions. This special session on graph learning will solicit recent advances that exploit various topics to benefit the solving of real-world problems.

The special session we propose is an excellent opportunity for the machine learning community and IJCNN 2023 to gather together and host novel ideas, showcase potential applications, and discuss the new directions of this remarkably successful research field. In particular, the special session will attract papers proposing deep learning models and methods for graphs, e.g., graph coarsening, structure learning, graph kernels and distances, and graph stream processing. Theoretical results, benchmarks, and practical applications are also welcome and encouraged.

Topics

This session focuses on the broad spectrum of machine learning methods for structured and relational data, with a focus on deep representation learning. Theoretical and methodological papers are welcome from any of the following areas, including but not limited to:

• Graph representation learning

• Graph generation (probabilistic models, variational autoencoders, adversarial learning, etc.)

• Graph learning and relational inference

• Graph coarsening and pooling in graph neural networks

• Graph kernels and distances

• Scalability, data efficiency, and training techniques of graph neural networks

• Tensor methods for structured data

• Theory of graph neural networks (e.g., expressive power, learnability, negative results)

• Learning on complex graphs (e.g., dynamic graphs and heterogeneous graphs)

• Automatic graph machine learning

• Relational reinforcement learning

• Anomaly and change detection in graph data

• Reservoir computing and randomized neural networks for graphs

• Recurrent, recursive, and contextual models

• Graph datasets and benchmarks

We also encourage application papers focused on but not limited to:

• Bioinformatics (e.g., drug discovery and protein folding)

• Cybersecurity (e.g., fraud detection)

• Transportation Systems (e.g., traffic forecasting)

• Recommender Systems (e.g., dynamic link prediction)

• Graph Machine Learning Platforms and Systems

• Computer Vision (e.g. point clouds)

• Natural Language Processing

Important Dates

Paper Submissions: February 7th, 2023 (11:59 PM AoE)
Paper Acceptance Notifications: March 31, 2023
Conference: June 18-23, 2023

Session Organisers

• Davide Bacciu (University of Pisa)

• Daniele Castellana (University of Florence)

• Federico Errica (NEC Laboratories Europe)

• Ming Jin (Monash University)

• Yixin Liu (Monash University)

• Nicolò Navarin (University of Padua)

• Shirui Pan (Griffith University)

• Luca Pasa (University of Padua)

• Senzhang Wang (Central South University)

• Feng Xia (RMIT University)

• Daniele Zambon (Swiss AI Lab IDSIA, Università della Svizzera italiana)

• Yizhen Zheng (Monash University)

For any enquire, please write to daniele.zambon [at] usi.ch or federico.errica [at] neclab.eu