1. Social Networks and Related Applications
李漢銘
臺灣科技大學資訊工程系
中央研究院資訊科學研究所

2. Outline What is a social network Why social networks
History of social networks
Social network analysis
Related applications
Related resources
Related keywords
References

3. What is a social network?
A set of dyadic ties, all of the same type, among a set of actors
Actors can be persons, organizations, groups
A tie is an instance of a specific social relationship

4. Why social networks?
Social network theory produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors.
This approach has turned out to be useful for explaining many real-world phenomena.

5. What can social networks help ?
How does a kind of fashion become an vogue?
How does a virus spread and infect people?
How does a research topic become a hot topic

6. History of social networks
1967: Small World Phenomenon (Stanley Milgram)
1974: The Strength of Weak Ties (Mark Granovetter)
1998: Collective Dynamics of Small-World (Duncan J. Watts and Steven H. Strogatz)
2003: Friendster (An online community that connects people through networks of friends for dating or making new friends )
Now: There are thousands of applications applied to social networks

7. Six Degrees of Separation
1967: Small World Phenomenon (Stanley Milgram)

8. First Network Model on the Small-world Phenomenon

9. Strong Link V.S. Weak Link
Bob
Mary

10. The Strength of Weak Ties
1974: The Strength of Weak Ties (Mark Granovetter)
Strong ties are your family, friends and other people you have strong bonds to.
Weak ties are relationships that transcend local relationship boundaries both socially and geographically.
Weak ties are more useful than strong ties

11. Friendster
An online community that connects people through networks of friends for dating or making new friends

12. Social network analysis
The shape (Sociogram) of the social network helps to determine a network's usefulness to its individuals.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, animals, etc.

13. An example of sociogram.
A is at the centre of two
subgroups of linked nodes
consisting of B, C, and D,
and E and F, respectively. A
also has a connection to G.
A connects to E, but E does
not connect to A.

14. How to do social network analysis
There are three key principles in social networks.
Degree
Density
Centrality

15. Degree in social networks

16. Density in social networks

17. Centrality in social networks
Degree Centrality
Closeness Centrality
Betweeness Centrality

18. Related applications Matthew Effect Internet Structure Anti-Spam
Infectious Disease Protection
Motif Finding

19. Matthew Effect
The rich get richer and the poor get poorer

20. Internet Structure

21. Internet Structure (cont)
Internet structure is also a small world
It possess a scale-free topology
A data transferred from a computer to another computer only needs four step (Four Degrees of Separation)

22. Anti-Spam Leveraging social networks to fight spam
network has been found with a scale-free topology
Find the spammer through centrality of social network

23. What is Spam?
Spam: equivalent of junk mail, unsolicited and undesired advertisements and bulk messages.
Spam Characters
Distribution
Sent to Millions
Can be targeted
Good
Credibility
Capability

24. Honey Pot Statistics of Spam
Data Source:

25. Social Email Network The email network has a low diameter.
The mean shortest path length in the giant connected component to be 4.95 for a component size of nodes

26. Email Scale-free network
Making use of the high clustering, commercial providers can identify communities of users more easily, and focus marketing more efficiently

27. Personal E-mail Networks.
In the largest
component ,
none of nodes
share neighbors

28. Personal E-mail Networks (cont).
Subgraph of a spam component.
Two spammers share many
corecipients (middlenodes). In
this subgraph, no node shares a
neighbor with any of its
neighbors.
Subgraph of a nonspam
component. The shows a higher
incidence of triangle Structures
(neighbors Sharing neighbors)
than the spam subgraph.

29. Infectious Disease Protection
How does our social network structure influence the spreading of the disease?
Whether our knowledge of network help us to fight this kind of disease?

30. Infectious Disease Protection (cont)

31. Infectious Disease Protection (cont)
Disease is tipped anytime in a scale-free network
Coexisting with disease is a new concept in modern disease protection
To control the connectors in networks can avoid disease exploded

32. Motif Finding
motif
Subgraphs that have a significantly higher density in the observed network than in the randomizations of the same.
Real network vs random networks

33. Related resources Social networks - Wikipedia, the free encyclopedia
How to do social network analysis
International Network for Social Network Analysis (INSNA)
NetLab (provides up-to-date information
on social networks in the broadest sense)

34. Related resources (cont)
[Tools]
InFlow (Social Network Mapping Software)
NetMiner (SNA Software)
UCINET (SNA Software)
International Network for Social Network Analysis

35. Related resources (cont)
[book] Mark Buchanan,NEXUS:small worlds and the groundbreaking science of networks. 中文譯本:連結

36. Related resources (cont)
[book] Duncan J. Watts, SIX DEGREES: The Science of a Connected Age. 中文譯本:6個人的小世界

37. References
[1][web] Jobs and the strength of weak ties, “
[2][web] Social network - Wikipedia, the free encyclopedia, “
[3][book] Mark Buchanan,NEXUS:small worlds and the groundbreaking science of networks
[4] Stanley Milgram, “ Small World Phenomenon , ” Psychology Today,1,60-67(1967)

38. References (cont)
[5]Duncan J. Watts and Steven H. Strogatz, “Collective Dynamics of Small-World Networks,” Nature 393, (1998)
[6] P. O. Soykin and V. P. Roychowdhury, “Leveraging social networks to fight spam,” IEEE Computer, 38(4):61-68, April 2005
[7] Churchill, E.F.; Halverson, C.A.; “ Guest Editors' Introduction: Social Networks and Social Networking,” Internet Computing, IEEE Volume 9,  Issue 5,  Sept.-Oct Page(s):