Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Understand

Many interesting problems in machine learning are being revisited with new deep learning tools.

• For graph-based semisupervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers.
• Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires a considerable amount of labeled data for validation and model selection.
• In this paper, we develop deeper insights into the GCN model and address its fundamental limits.