Providing over 20 network datasets and Python tutorials specifically curated for the textbook 'A First Course in Network Science' by Menczer, Fortunato, and Davis. The data includes edge lists and node attributes for diverse systems such as social interactions, biological pathways, and technological infrastructures used for educational demonstrations.
Use Cases
- Calculate centrality measures like PageRank or betweenness using the provided edge list files
- Implement community detection algorithms using the node attributes and connectivity patterns
- Visualize complex graph structures using the tutorial scripts and sample network data provided
Strengths
- Includes edge list files (.txt or .csv) for various real-world networks like social circles and power grids
- Provides Jupyter notebook tutorials demonstrating network analysis using the NetworkX library
- Contains node-level metadata and edge weights to support attribute-based network modeling