Machine Learning and Graph Neural Networks

Dear Colleagues,

The research field of machine learning for graphs studies the application of well-known machine learning concepts to the processing of graph-structured data. Graphs are abstract objects that naturally represent interacting systems of entities, where interactions denote the functional and/or structural dependencies between them. Graph Neural Networks are a recent family of machine learning models specifically designed to harness the inherent structure and dependencies present in graph-structured data, revolutionizing the way we analyze, model, and make predictions in complex networked structures. The Special Issue aims to cover applications where machine learning and Graph Neural Networks have proven to be effective. We invite authors from academia and industry to contribute their original research articles, surveys, and high-quality review papers that demonstrate the effectiveness of machine learning and Graph Neural Networks in solving real-world problems while showcasing the latest developments and novel applications. 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.

We encourage submissions that address, but are not limited to, the following areas:

• Machine learning;
• Graph Embeddings;
• Deep Learning on graphs (Graph Convolutions, Graph Attention Networks, Graph Autoencoders, and Graph Spatial–Temporal Networks);
• Learning on dynamic, temporal, and/or complex graphs;
• Knowledge modeling/representation in/for graph learning;
• Novel models and algorithms for graphs;
• Node/graph Classification/prediction;
• Wired/Wireless Communication Networks;
• Internet of Things;
• Natural Language Processing;
• Computer Vision;
• Recommendation Systems;
• Other areas (Mobility/Transportation, Geographical, Financial, and Robotics/Cyber–physical).

Prof. Dr. Shuai Zhao
Guest 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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

• machine learning
• graph neural networks
• deep learning
• Internet of Things
• computer vision