Mechanism and Modeling Research 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 biomedicine, genetical analysis, and pattern recognition. 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 the tasks including node classification, graph classification, and link prediction. Moreover, GCNs can not only be used to learn node representations capturing the topology between the data, but can also be utilized as features for downstream tasks, like classification and clustering. However, various issues 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, it needs to consume a lot of storage space to store the graph structure, making it important to consider the size of the graph. Third, it is important to consider the different kinds of data for specific tasks in homogeneous graphs or heterogeneous graphs. To deal with the discussed issues and the existing research challenges, this Special Issue aims to encourage scholars to design interesting works based on GCNs and to explore the mechanism and modeling of the framework of GCNs. Moreover, high-quality submissions including theory analysis and interpretability of GCNs are welcome.

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

• Theory construction and analysis of GCNs;
• Kernel-based, metrics-based, 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 model;
• Safety and reliability of GCNs with representation learning;
• Sub-graph learning for GCNs;
• Federated learning in GCNs model;
• Homogeneity graphs and heterogeneity graphs for GCNs.

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

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• theory construction and analysis of GCNs
• kernel-based, metrics-based, 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 model
• safety and reliability of GCNs with representation learning
• sub-graph learning for GCNs
• federated learning in GCNs model
• homogeneity graphs and heterogeneity graphs for GCNs