eririsawamura / co-authorship-network-analysis

This project aims to use network analysis techniques on a moderate-sized network dataset. Ex: <year>,<volume>,<journal>, <author1>& ... &<authorN>, <title> You can find the bibliographic dataset at: http://snap.stanford.edu/na09/hep-th.txt.

Co-authorship-Network-Analysis-

This project aims to use network analysis techniques on a moderate-sized network dataset. Ex: <year>,<volume>,<journal>, <author1>& ... &<authorN>, <title> You can find the bibliographic dataset at: http://snap.stanford.edu/na09/hep-th.txt. Basic function:

1. Network degree distribution.
2. Clustering co-efficiency.
3. Shortest path length.
4. Network centrality.
5. Network connectivity.
6. Node similarity.

Advanced function: Community discovery

1. K-clique detection algorithm
2. Modularity-based detection algorithm
3. Description of the Girvan_newman detection algorithm

UI √ Visualization √

Requirements: matplotlib: command: python -m pip install -U matplotlib networkx: command python -m pip install -U networkx