1. 資訊網絡分析 Social Network Analysis
ftp:// /nplu/
資訊網絡分析 Social Network Analysis

2. 資訊網絡分析 Information Network Analysis
Textbook
Hanneman, R. & Riddle, M. (2005), Introduction to Social Network Methods, University of California, Riverside.
References
Easley, D. & Kleinberg, J. (2010), Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge University Press.
Borgatti, S.P., Everett, M., & Johnson, J.C. (2013), Analyzing Social Networks, SAGE Publications.
Grading
Midterm Quiz (15%)
Midterm Report (20%)
Final Exam. (25%)
Final Report (25%)
Homeworks, etc. (15%)

3. Attention, Please! AACSB AoL Learning Goal:
20 min
conclusion
Time
AACSB
AoL Learning Goal:
1. Students will grasp the knowledge and understand applications of information technology.
1-1. Students will grasp the knowledge of information technology.
1-2. Students will demonstrate an understanding of information technology applications.

4. Business Readings Net Smart: How to Thrive Online
Alone Together: Why We Expect More from Technology and Less from Each Other
The App Generation: How Today’s Youth Navigate Identity, Intimacy, and Imagination in a Digital World
It’s Complicated: The Social Lives of Networked Teens
Dot complicated: Untangling Our Wired Lives
The Digital Divide
Too Big To Know
Everything is Miscellaneous: The Power of The New Digital Disorder
Net Smart: How to Thrive Online

5. Popular Science Readings
Six Degrees: The Science of a Connected Age
Superconnect
Everything Is Obvious: Once You Know the Answer
Social Physics: How Good Ideas Spread ─ The Lessons from a New Science
Linked
Connected
Bursts: The Hidden Patterns Behind Everything We Do, from Your to Bloody Crusades

6. Advances of Information Technologies

9. From Websites to Web Applications (APPs)

10. Usages of Social Media

11. Rhythms of social interaction: Messaging within a massive online network (Golder et al., 2007)
Data:
Facebook (
Result:

12. Golder et al. (2007) (2/2)
fat-tail
distribution

13. Rhythms of Blogging

14. Rhythms of PTT

15. Rhythms of Wikipedia

16. Rhythms of Yahoo!Kimo Auction
賣方刊登商品時間
買方得標商品時間
針對77家抽樣商店2006/7/1~2006/12/31的交易資料，在這六個月期間共產生了108383次交易
其中星期一所佔的比例最高，而星期六、日的比例最低
顯示出一天中，中午及下午時段與午夜12點賣方刊登商品較活躍
以77家抽樣商店2006/7/1~2006/12/31的交易資料，在這六個月期間因交易關係產生 位買方(未去除重覆買方)，
星期一至星期五所佔比例最高，而星期六、日所佔比例最低。
進一步的以24小時表示，顯示出買方購買商品時間主要以中午、晚上比例最高
由此推測出這些買方為星期一到星期五，在下班之後才能上網拍賣競標的業餘買方。

17. Rhythms of funP
星期二新增書籤個數較多
星期六、日新增書籤個數較少

18. Rhythms of Plurk
Good night

19. Suggested Readings (ftp://163.25.117.117/nplu)
In the directory: /資訊網絡分析/Readings
01 Social Media Networks.pdf
02 Rhythms of social interaction.pdf
T?? *.pdf

20. What is a Network?

21. Introduction Many real world systems can be described as networks.
Social relationships:
e.g. interaction in social media, collaboration in academic, entertainment, business area.
Technological systems:
e.g. internet topology, WWW, or mobile networks.
Biological systems:
e.g. regulatory, metabolic, or interaction relationships.
And so on and so forth…

22. A Simple Network
Node degree: 4
Path length: 2

23. Other Example Networks

24. An example network showing community structure

25. A network of collaborations among scientists (Newman, 2011)

26. Almost perfect polarization in the network (Sampson, 1968)

27. Friendship network of children in a US school (Moody, 2001)

28. Communities of political blogs (Adamic and Glance, 2005)

29. The Internet (Lumeta Corp, 2007)

30. Types of Network Ties

31. Network Theory and Theory of Networks
Borgatti, S. P. & Halgin, D. S. (2011). On network theory.
Organization Science, 22(5),

32. Suggested Readings (ftp://163.25.117.117/nplu)
In the directory: /資訊網絡分析/Readings
03 Complex networks.pdf
04 On network theory.pdf
05 Network theory.pdf
06 Communities modules and large-scale structure in Network.pdf

33. UCINET: A Tool for Social Network Analysis

34. UCINET Basics Official Site of UCINET Official UCINET Tutorial
Official UCINET Tutorial

35. Install UCINET Get the software Then,
Then,
Press next, next, and next…
Finally, the software is installed
Registration is required to use UCINET legally

36. The UCINET Environment
Main menu
File
Data
Transform
Tools
Network
Many analysis procedures are here
Visualize
NetDraw
Options
Help
Contents: Introduction Section, DL, and Standard Datasets.
Index: search by keywords.

37. A Typical Analysis Procedure
Select a procedure from the Network menu

38. Lab 0: A Short Tour in UCINET
Open UCINET
Then have a good time… I hope so…

39. Other Tools (ftp://163.25.117.117/nplu)
In the directory: /資訊網絡分析/Readings
07 Pajek.pdf
08 NodeXL.pdf
09 E-Net.pdf

40. Network Science: A Multi-discipline Research Field

41. The Invasion of the Physicists
First shot: Small-world networks
Watts, D.J. and Strogatz, S.H. (1998), “Collective dynamics of ‘small-world’ networks,” Nature, 393,
Second shot: Scale-free networks
Barabási, A.-L. and Albert, R. (1999), “Emergence of scaling in random networks,” Science, 286,
(Social) network analysis in
Social physics
System biology
Computational social science
with supercomputer
with computing cloud
The defense of the social scientist
Linton C. Freeman, 2008, “Going the Wrong Way on a One-Way Street: Centrality in Physics and Biology,” Journal of Social Structure, Vol. 9.

42. Small-world Network The small-world effect (Milgram, 1967)
Six degrees of separation in USA
Watts-Strogatz model (Watts and Strogatz, 1998)
Rewire links from regular to random networks

43. Path Lengths and Clustering Coefficients
General
Graph
Regular
Random
Characteristic Path Length
Clustering Coefficient
n: number of nodes in the network
d(i, j): shortest path length between nodes i and j
ki: degree of node i
K: average node degree of the network
ei: number of links between the neighbors of node i

44. Comparison of path lengths and clustering coefficients
Link Rewire Probability

45. Some Small-world Networks
Newman et al. (2001)
low
path length
high
clustering coefficient

46. Degree Distributions of Networks
Poisson distribution
Power-law distribution
Z = (n - 1) p Z
fat-tail
distribution

47. Scale-free Network Power-law degree distributions
Scale Free Model (Barabási and Albert, 1999)
Incremental growth: The network is growing continuously by adding new nodes or new connections step by step
Preferential connectivity: Highly connected nodes are more likely to be connected again in the process of incremental growth, also called the rich-get-richer phenomenon;

48. Some Scale-free Networks

49. Properties of Scale-free Networks
Power law degree distribution: Rich get richer
Small World: A small average path length
Mean shortest node-to-node path
Can reach any nodes in a small number of hops, 5~6 hops
Modularity: A large clustering coefficient
How many of a node’s neighbors are connected to each other
Robustness: Resilient and have strong resistance to failure on random attacks and vulnerable to targeted attacks
Disassortative or Assortative
Biological networks: disassortative
Social networks: assortative

50. Roubstness of Scale-free Networks

51. Assortativity of Scale-free Networks

52. Comparison

53. Suggested Readings (ftp://163.25.117.117/nplu)
In the directory: /資訊網絡分析/Readings
10 Collective dynamics of 'small-world' networks.pdf
11 Emergence of Scaling in Random Networks.pdf
12 Computational social science.pdf
13 Going the Wrong Way on a One-Way Street.pdf
14 ScaleFree_Scientific Ameri 288, (2003).pdf
15 The physics of networks.pdf
16 The Invasion of the Physicists.pdf