DATASET.md

Cora Citation Network

The Cora dataset is a graph dataset that represents a network of scientific publications and their citations. It is widely used in machine learning research for tasks involving graph structures, such as node classification, clustering, and link prediction.

Dataset Details:

• Nodes: 2,708 scientific papers.
• Edges: 5,429 citation relationships.
• Features: A 1,433-dimensional feature vector representing the bag-of-words representation of each paper.
• Classes: Papers are classified into one of 7 categories:
  • Case-Based
  • Genetic Algorithms
  • Neural Networks
  • Probabilistic Methods
  • Reinforcement Learning
  • Rule Learning
  • Theory

Structure:

The dataset is structured as a single graph:

• Adjacency Matrix (A): Encodes the connections between nodes (citations).
• Feature Matrix (X): Stores feature vectors for each node (bag-of-words).
• Label Vector (Y): Contains the ground truth label for each node.

Preprocessing:

The dataset was preprocessed as follows:

1. Added self-loops to the adjacency matrix to improve model learning.
2. Split into:
   • Training set (85%)
   • Validation set (10%)
   • Testing set (5%)
3. Negative sampling: Non-existent edges were sampled to create a balanced dataset for link prediction tasks.

Access:

The dataset is available for download from:

• PyTorch Geometric
• Kaggle

Visualization

Below is an example of the Cora citation network. Nodes represent papers, and edges represent citation relationships.

(Include a visualization image here if available, e.g., cora_graph.png)

Citation:

If you use this dataset, please cite:

@article{mccallum2000automating,
  title={Automating the construction of internet portals with machine learning},
  author={McCallum, Andrew and Nigam, Kamal and Rennie, Jason and Seymore, Kristie},
  journal={Information Retrieval},
  volume={3},
  number={2},
  pages={127--163},
  year={2000},
  publisher={Springer}
}