This project dives into the world of network science through hands-on analysis of a real-world social network dataset. Conducted as part of our university coursework, it involved:
- Constructing and visualizing a social graph
- Calculating key network statistics (nodes, edges, density, avg. degree)
- Determining whether the network is directed
- Running deeper analyses like centrality, clustering, and community detection
We uncovered the structure and behavior of a complex social network, revealing insights into how people are connected and which individuals hold influential positions in the group. Our findings included:
- Clear identification of the network type (directed/undirected)
- Key nodes with high centrality scores (i.e., "influencers")
- Community structures within the network
Nikol Tushaj
Rajla Culli
Chloe Monique Quevedo
Lina Kolevska
Jenny Maniciati