andberto / Euroroad

European road network analysis

Computer science and engineering

Project Assignment - Complessità nei sistemi e nelle reti - Politecnico di Milano (March 2023)

📖 Table of Contents

Table of Contents

1. ➤ About The Project
2. ➤ Project Files Description
3. ➤ Graph analysis
4. ➤ References

📝 About The Project

The focus of the project, is to analyze the European road network graph with the purpose of discovering interesting insights.

The graph represents the European road network :

• A node represents a city.
• An edge represents a road directly connecting two cities.

There are 1174 nodes (cities), connected by 1417 edges (direct roads), The network is undirected and unweighted.
The raw dataset can be found here, check also the profile of the author here.

💾 Project Files Description

• GephiParser.py: a simple script to parse some data in Gephy format.
• CoordsRetriever.py: it automatically retrieves cities coords using OpenStreetMaps API.
• Euroroad.py: the main script.

🔸 Graph analysis

Generic stats

Considering the average distance (≈18) and the degree distribution we can state that the network is neither small word nor scale-free, moreover the network has almost tree-like structure and it’s very sparse.

Community analysis

The key point of the project is the community analysis, some algorithms are evaluated. The analysis revealed a strong community structure.

Louvain method	Peristence probabilities alfa=0.85	Peristence probabilities alfa=0.7

For the complete analysis check the EuroRoad.pdf file !!

🔸 References

[1] Robust network community detection using balanced propagation, L. Šubelj et. Al, 2011 (arxiv.org)
[2] Profiling core-periphery network structure by random walkers, F. Della Rossa et al, 2013. (nature.com)
[3] Fast unfolding of communities in large networks, Blondel et al. 2008. (arxiv.org)
[4] Finding and testing network communities by lumped Markov chains, C. Piccardi, 2011. (arxiv.org)
[5] Maps of random walks on complex networks reveal community structure, M. Rosvall, C. T. Bergstrom, 2008. (arxiv.org)
[6] Near linear time algorithm to detect community structures in large-scale networks, U. N. Raghavan et al., 2007. (arxiv.org)
[7] Towards real-time community detection in large networks, I. X. Y Leung et al., 2009. (arxiv.org)