Network & Graph Analysis in Excel: Degree, Paths & Centrality
Edge lists and adjacency matrices with COUNTIFS, degree, strength and density, a Dijkstra step table, common neighbours with matrix multiplication, closeness, PageRank iterations and edge betweenness: 8 graded labs on a fictional studio network.
The first lesson is free — no account needed.
About this course
A network and graph analysis course that maps one fictional design studio's chats, courier routes and collaborations in a spreadsheet, for data analysts, students of social network analysis and anyone curious how PageRank works. You turn a chat export into an adjacency matrix with COUNTIFS and count the mutual ties, measure in-degree, out-degree and normalised degree centrality to find the hub, add message weights for strength and network density, run Dijkstra's shortest-path algorithm in a step table and recover the route, square the collaboration matrix for common neighbours and two-step reach, turn a distance matrix into closeness centrality and the average path length, iterate PageRank with a damping factor of 0.85 until the ranks settle and find the bridge between two groups with edge betweenness and a degree distribution chart. The first lab is free.
Lessons
- From Edge List to Adjacency Matrix with COUNTIFSFree
- Degree, In-Degree & Out-Degree: Who Is the Hub? Pro
- Weighted Degree (Strength) & Network Density Pro
- Shortest Paths by Hand: a Dijkstra Step Table Pro
- Two-Step Reach & Common Neighbours with Matrix Multiplication Pro
- Closeness Centrality from a Distance Matrix Pro
- PageRank by Iteration: Damping 0.85 in a Step Table Pro
- Bridges, Communities & the Degree Distribution Pro