Connectionists: [CFP] Special Session: Deep Learning for Graphs @ IEEEWCCI24

[Davide Rigoni]

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CALL FOR PAPERS
IEEEWCCI24 IEEE World Congress on Computational Intelligence:
https://2024.ieeewcci.org/

More details on the special session "*Deep Learning for Graphs*"*: *
https://sites.google.com/view/dl4g-2024/home

Yokohama, Japan, 30 June–5 July 2024
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*** 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 is an excellent opportunity for the machine
learning community to gather together and host novel ideas, showcase
potential applications, and discuss the new directions of this remarkably
successful research field.

*** Important Dates **

Paper Submissions (EXTENDED): January 29th, 2024
Paper Acceptance Notifications: March 15, 2024
Conference: June 30 - July 5, 2024


*** 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.)ù
   - Graph learning and relational inference
   - Graph coarsening and pooling in graph neural networks
   - Graph kernels and distances
   - Theory of graph neural networks (e.g., expressive power, learnability,
   negative results)
   - Learning on complex graphs (e.g., dynamic graphs and heterogeneous
   graphs)
   - Deep learning for dynamic graphs and spatio-temporal data
   - Anomaly and change detection in graph data
   - Reservoir computing and randomized neural networks for graphs
   - Recurrent, recursive, and contextual models
   - Neural algorithmic reasoning
   - Relational reinforcement learning
   - Automatic graph machine learning
   - Scalability, data efficiency, and training techniques of graph neural
   networks
   - Tensor methods for structured data
   - 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

*** Submission Instructions ***

   1. Go to the IEEE WCCI 2024 website <https://2024.ieeewcci.org/> and
   click on "Submit your paper".
   2. You will be redirected to EDAS. Log into the system.
   3. Select "IJCNN 2024 Special Session Papers"
   4. Insert details of your paper and select the topic "Special Session:
   Deep Learning for Graphs"
   5. Click on "Register Paper". Good Luck!!


*** Session Organisers ***

   - Nicolò Navarin <https://sites.google.com/view/nicknavarin> (University
   of Padua)
   - Davide Bacciu <https://pages.di.unipi.it/bacciu/> (University of Pisa)
   - Daniele Zambon <https://dzambon.github.io/> (Swiss AI Lab IDSIA,
   Università della Svizzera italiana)
   - Federico Errica <https://diningphil.github.io/> (NEC Laboratories
   Europe)
   - Daniele Castellana <https://danielecastellana22.github.io/> (University
   of Florence)
   - Luca Pasa <https://sites.google.com/view/lpasa-math-unipd/> (University
   of Padua)
   - Davide Rigoni <https://www.drigoni.it/> (University of Padua)
   - Filippo Maria Bianchi
   <https://sites.google.com/view/filippombianchi/home> (UiT the Arctic
   University of Norway)
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