Social Networks: A Basic Introduction in Four Minutes
1. Two Elements in Networks

Tie: Ties represent relations between nodes

Node: Nodes represent things that relate somehow to one another

2. Two Kinds of Networks
Directed Networks: Networks in which the tie has direction (AKA "digraphs"). Examples: hitting, kissing, infecting, sending letters.

Undirected Networks: Networks in which the tie has no direction. Examples: playing tennis with, married to, codefendant

3. Distance: "Geodesic" (Shortest Path)

4. Network Density
Directed Networks: # actual ties / (n*(n-1))
Undirected Networks: # actual ties / (n*(n-1))/2

where n=# of nodes

4. Closeness Centrality:
= 1/Farness
Farness: Sum of Distance to all other Nodes

5. Degree centrality: how many ties touch a node?

6. Betweenness Centrality for Node X:
Sum for all pairS of nodes (of the fraction of geodesics between A pair of nodes that have Node X in the middle)

7. Ego Networks
Level 1.0: Ego's Ties to Alters
Level 1.5: Level 1.0 Plus Alters' Ties to Other Alters
Level 2.0: Level 1.5 Plus Alters' Ties to Alters' Alters

8. Induced Homophily:
A tendency for ties to form to similar others because similar others are especially present in the social environment (group, community, society)
Example: No wonder blues are mostly tied to blues... there are hardly any reds out there!

9. Choice Homophily:
A tendency to choose to form ties with similar others even when different others are available in the social environment (group, community, society)
Lots of blues and lots of reds out there, Yet each is mostly tied to its own kind!

10. Kozo Sugiyama's Network Design Principles in the abstract
• Ties should be easy to follow from node to node
• Ties should be far from one another
• Ties should not cross or touch
• Ties should be straight
• Nodes that connect should be close
• Similar nodes should be close
• Central nodes should be in the center


Sugiyama, Kozo. 2002. Graph Drawing and Applications for Software and Knowledge Engineers. Singapore: World Publishing Company Inc.

That's easy to do in small, abstract networks...
... but large, real-world networks pose a challenge.

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Undergraduate Social Networks at uma.edu