Mechanism and Modeling of Graph Convolutional Networks

Dear Colleagues,

Graph convolutional networks (GCNs) have been developed rapidly, leading to the creation of diverse models in different fields, such as the biomedical, genetical analysis, and pattern recognition fields. GCNs are a type of deep learning model that operate on graph-structured data, as they can capture the local structure of data and identify patterns and regularities in the data based on tasks including node classification, graph classification, and link prediction. Moreover, GCNs not only can be used to learn node representations capturing the topology between the data, but can be utilized as features for downstream tasks, such as classification and clustering. However, there are various issues that can be found in GCNs. First, it is not convenient to predict the unseen data since the designed graph only considers the correlation for the training data. Second, GCNs need to consume a lot of storage space to store the graph structure, and thus, it is important to consider the size of the graph. Third, for the homogeneous graph or the heterogeneous graph, it is important to consider the different kinds of data for the specific tasks. To deal with the discussed issues and the existing research challenges, this Special Issue aims to encourage scholars to design more interesting works based on GCNs and to explore the mechanism and modeling of the framework of GCNs. Moreover, high-quality submissions involving theory analysis and the interpretability of GCNs are welcome.

Below is an incomplete list of potential topics to be covered in the Special Issue:

• Theory construction and analysis of GCNs;
• Kernel-based, metric-based, and causal inference-based learning for GCNs;
• Explainable representation learning for GCNs;
• Supervised, semi-supervised, unsupervised, transfer, and reinforcement-based learning for GCNs;
• Missing information imputation of GCN models;
• Safety and reliability of GCNs with representation learning;
• Sub-graph learning for GCNs;
• Federated learning in GCN models;
• Homogeneity graphs and heterogeneity graphs for GCNs.

Dr. Rongyao Hu
Dr. Tong Liu
Dr. Jiong Wu
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 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.