Abstract: With the wide application of graph neural network (GNN) in many fields, how to extract and aggregate node features effectively has become a hot research issue. In this paper, we propose a ...

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is ...

In this project, we develop a GCN model to classify nodes within a social network graph. The dataset contains features for each user (node), edges representing relationships, and target labels ...

Not every sample of data can be meaningfully plotted on a two-dimensional graph. MATLAB, a technical analysis software suite from MathWorks, allows you to plot publication-quality, three-dimensional ...

Traditional neural networks don't take into account the relational inductive bias inherent in graph-structured data. GCNs solve this by aggregating features from a node's neighbors during learning, ...