Graph Convolutional Networks Model

The Graph Convolutional Networks (GCN) model is a deep learning model designed specifically for learning from graph-structured data. It extends the concept of Convolutional Neural Networks (CNNs) to graphs by performing convolutions on graph nodes or edges, thereby capturing the local and global graph structure. GCN can be used for tasks like node classification, link prediction, and graph-level classification.

1. Social Network Analysis: GCN can be applied to analyze social networks and predict missing links, identify influential users, or detect communities within the network.
2. Recommendation Systems: GCN can be used to discover latent features and connections in user-item interaction graphs, which can effectively improve recommendations for users.
3. Bioinformatics: GCN has been successfully applied to predict protein-protein interactions, classify biological compounds, and analyze molecular structures.

• Graph Convolutional Networks - Tutorial by Thomas Kipf, one of the authors of the original GCN paper.
• Deep Learning on Graphs: A Survey by Zonghan Wu, et al. This survey provides a comprehensive overview of different graph convolutional architectures and their applications.
• Spektral - A Python library for graph deep learning that provides easy implementation of various graph convolutional models, including GCN.

1. Thomas Kipf - Co-author of the original GCN paper and has contributed significantly to the field of graph convolutional networks.
2. Jure Leskovec - A prominent researcher and practitioner in the field of graph mining and analysis, with expertise in applying GCN to various domains.
3. Petar Veličković - Notable for his contributions to graph neural networks, including GCN and GraphSAGE.