Connectionists: [CfP - Deadline Approaching] Deep Learning for Graphs -- ESANN 2022

[Federico Errica]

Call for Papers: "Deep Learning for Graphs"

Special Session of the European Symposium on Artificial Neural
Networks – ESANN 2022

5 - 7th October 2022, Bruges, Belgium (and online)
Conference Page: https://www.esann.org/

Papers submission deadline: May 9, 2022 (AoE).
Papers Submission System: https://www.esann.org/node/6


Description

Traditional deep learning approaches have been developed assuming data
to be encoded into feature vectors, however many important real-world
applications generate data that are naturally represented by more
complex structures, such as graphs. Graphs are particularly suited to
represent relations between the components constituting an entity,
allowing us to effectively describe systems of interacting elements,
like social, biological, and technological networks, as well as data
where topological variations influence the feature of interest, e.g.,
the interaction of proteins or molecular compounds.
This has motivated a recent increasing interest of the machine
learning community in the development of learning models for
structured information.
The field of graph deep learning, in particular, combines the ability
of deep neural networks to learn representations end-to-end with this
explicit description of relations in the data. Specifically, the class
of models at the heart of graph deep learning, generically called
Graph Neural Networks (GNNs), extend and generalize typical
convolutional neural networks to process arbitrary graphs.


Topics

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

-  Graph Neural Networks: theory and applications
-  Graph representational learning
-  Graph generation (probabilistic models, variational autoencoders,
adversarial learning, etc.)
-  Graph learning and relational inference
-  Graph kernels and distances
-  Scalability, data efficiency, and training techniques of graph
neural networks
-  Deep learning for dynamic graphs and graph sequences
-  Reservoir computing and randomized neural networks for graphs
-  Recurrent, recursive and contextual models
-  Graph datasets and benchmarks
-  Applications in natural language processing, computer vision (e.g.
point clouds), materials science, cheminformatics, computational
biology, social networks, etc.


Important Dates:

Papers submission deadline:  May 9, 2022 (AoE).
Decision Notification to Authors: by July 19, 2022


Organizers

Luca Pasa, University of Padova (IT)
Nicolò Navarin, University of Padova (IT)
Daniele Zambon, Università della Svizzera Italiana (CH)
Davide Bacciu, University of Pisa (IT)
Federico Errica, NEC Laboratories Europe (DE)