Usage and Applications
The Cora dataset is extensively used for evaluating graph-based machine learning algorithms. Its applications span several key areas:
- Node Classification: Predicting the class of each node (paper) based on its features and the graph structure. Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) are examples of models tested using the Cora dataset.
- Link Prediction: Inferring missing links or predicting future citations between nodes. The Cora dataset serves as a benchmark for algorithms that analyze the likelihood of connections within the graph.
- Clustering: Grouping nodes into clusters with similar properties. The dataset’s community structure is ideal for testing clustering algorithms, helping to identify natural groupings within the network.
Cora Dataset
The Cora dataset stands as a fundamental resource in the field of graph machine learning, widely utilized for the development and benchmarking of various algorithms. Comprising a network of scientific publications in machine learning, the dataset provides a rich structure that facilitates research into node classification, link prediction, and clustering. This article presents an overview of the Cora dataset, its structure, applications, and the features and labels that define it.