1. Social Networks Analysis
Week 1

2. 2 TED Talks about SNA The Hidden Influence of Social Networks
How Social Networks Predict Epidemics
ocial_networks_predict_epidemics

3. Opview

4. Edge/Link/Relationship
Vertice/Node
Edge/Link/Relationship A B C
Clique
Social Network

5. A Simple Social Diagram
Nodes/Roles
Relationships
Directed/Undirected
One Way/ Two Way
Positive/Negative
Selt-Defined
A Simple Social Diagram

6. Social Network Analysis SNA

7. Terms/專有名詞 Social Networks
A Description of Relationship Between Items (human, animal, etc.)
A social network is a social structure to describe social relations (wikipedia)
Social Networking
Creating Social Networks or connecting people
Online Social Networking
Creating Social Networks or linking people via online environment ( , MSN, Line, WhatsApp)
Social Networking websites
Platforms for Social Networking (Facebook, Plurk, twitter)
Social Networks Analysis
Analysis the relationships of social networks
Social Networks Mining
Applying the techniques of data mining to analyze social networks
Social Computing
All applications related to social~

8. What is SNA?
A set of mathematical and statistical techniques for handling relational data
For identifying the structural properties of sets of relations (i.e. of networks)
And for visualizing and describing networks
Social scientific origins in: sociology, anthropology, social psychology
Mathematical bases in: graph theory, matrix algebra and (increasingly) statistics
There is an increasing dialogue with physics, maths, computer science, informatics

9. Cross-Fields Research

10. fROM sociology TO cOMPUTER sCIENCE
Sociologists only focus on small social networks
50~100 nodes in a social network
The advent of Internet communications has greatly increased SNA’s popularity
Computer & Information Technologies become essential tools for SNA (Churchill & Halverson 2005)

12. sna measurements Relational Data From Social Data to Relational Data
Nodes & Ties
Dyad
Triad
Subgroup
Group
Degree
Centrality and Power
Density
Path Length & Neighborhoods
Small World
Clustering Coefficient
Structural Hole
Clique

13. Relational Data

14. From Social Data to Relational Data

15. From Social Data to Relational Data
Collect Social Data
Identify the relationships
Identify the weights of each relationship
User matrix
Network Graph
Measurements

16. Six Degrees of Separation
The experiment:
Random people from Nebraska were to send a letter (via intermediaries) to a stock broker in Boston.
Could only send to someone with whom they were on a first-name basis.
Among the letters that found the target, the average number of links was six.
六度分隔理論
Six Degrees of Separation

17. Small-World
Most pairs of nodes seem to be connected by a short path through the network (Six degrees of separation)
Average path length (L): Mean path length between nodes in the network
Diameter (D): Maximum path length between nodes in the network
Small-world implies that spread of information will be fast

18. Six Degrees of Separation
“Everybody on this planet is separated by only six other people. Six degrees of separation. Between us and everybody else on this planet. The president of the United States. A gondolier in Venice… It’s not just the big names. It’s anyone. A native in a rain forest. A Tierra del Fuegan. An Eskimo. I am bound to everyone on this planet by a trail of six people…”
Six Degrees of Separation

19. sonam Applications Marketing & E-commerce Target Marketing
Collaborative Recommendation
Terrorist & Crime Detection
911 Network
Ipswich’s Jack the Ripper, England 2006
Medical Network
Finding Blood
Organ
Knowledge Management
Sharing (Finding Knowledge)
Innovation

20. sonam Applications Learning Organizational Social Network Analysis
Optimice
Politic & Election
Academic Social Networking
Family Tree
Game AI
On-line Game
Game with Social Network (Game 2.0)
Second Life
And Much More…………………