# Social Network Analysis (week one)

I started this course late, so I’m already a week behind. I’ll be doing some extra work this weekend to get caught up. All in all, it’s an interesting course!

The software being using for this course includes:

- Gephi = visualization and basic network metrics
- NetLogo = modeling network dynamics
- iGraph = for programming assignments

**Week 1 basic terms:**

Strongly Connected Components: A directed graphic is called strongly connected if there is a path from each node (aka vertex) in the graph to every other node. This means that a complete path should exist: a path from a to b and also a path from b to a. A path can also be looked as a complete loop. Wikipedia

In-degree Distribution: the number of incoming edges to a specific node.

The degree of a node in a network is the number of connections it has to other nodes and the degree distribution is the probability distribution of these degrees over the whole network.

If a network is directed, meaning that edges point in one direction from one node to another node, then nodes have two different degrees, the in-degree, which is the number of incoming edges, and the out-degree, which is the number of outgoing edges.

Directed edges: arcs, links

Undirected: mutual connections / emotions

Other edge attributes: weight (can be positive or negative), ranking, type, properties depending on the structure of the rest of the graph (e.g. betweenness)

Data representation: adjacency matrix, edge list, adjacency lists

P-star models – They’ve only been mentioned up to this point and we probably won’t cover it in the course, but I’m looking into it.

Two assignments are due on 21st Oct, which I’m working through this weekend. Hopefully the items we’ve covered so far stick!

*Above are notes I’ve taken to keep track of the material covered in the Social Network Analysis course I am taking on Coursera. Notes are derived from week-based video lectures and supplemental information I’ve found online to help clarify certain concepts. If you are taking this course, most of the information below will be found in Week 1 associated material. If you are not taking the course, I hope you find the information below helpful in your search*