Graph Machine Learning

Dear Colleagues,

Graph-structured data are ubiquitous in many fields and, in particular, electronics and computer science. Graphs allow modelling complex system, but to unlock the potential of these data, machine learning plays an important role. However, existing learning algorithms are mostly adapted to Euclidean (non-graph) structures. Therefore, there is an increasing interest in extending machine learning approaches for graph and manifold data.

In this Project Collection, we welcome submissions (both of research papers and reviews) related to machine and deep learning with graphs in computer sciences. The topics of interest include, but are not limited to:

• Learning representations of non-Euclidean data;
• Advanced information processing and architectures (graph neural networks, graph filtering, graph pooling, parameter learning, etc.);
• Training frameworks (unsupervised, semi-supervised, weakly, self- or supervised learning, as well as active learning, domain adaptation, or transfer learning);
• Theoretical aspects (expressive power, scalability trade-off, etc.).

Prof. Dr. Gemma Piella
Collection Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.