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Social Networks and Related Applications

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

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Social Networks and Related Applications

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  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 arethousands 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 • InternetStructure • 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 • Email 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 email messages. • Spam Characters • Distribution • Sent to Millions • Can be targeted • Good Email • Credibility • Capability

  24. Honey Pot Statistics of Spam Data Source: http://www.projecthoneypot.org/

  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 56969 nodes

  26. Email Scale-free network • Making use of the high clustering, commercial e-mail 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. 1000 random networks

  33. Related resources • Social networks - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Social_networking • How to do social network analysis http://www.orgnet.com/sna.html • International Network for Social Network Analysis (INSNA) http://www.sfu.ca/~insna/ • NetLab (provides up-to-date information on social networks in the broadest sense) http://www.chass.utoronto.ca/~wellman/netlab

  34. Related resources (cont) [Tools] • InFlow (Social Network Mapping Software) http://www.orgnet.com/index.html • NetMiner (SNA Software) http://www.netminer.com/NetMiner/home_01.jsp • UCINET (SNA Software) http://www.analytictech.com/ucinet_5_description.htm • International Network for Social Network Analysis http://www.insna.org/INSNA/soft_inf.html

  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, “http://joi.ito.com/archives/2003/08/16/jobs_and_the_strength_of_weak_ties.html” • [2][web] Social network - Wikipedia, the free encyclopedia, “http://en.wikipedia.org/wiki/Social_networking” • [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 393,440-442(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. 2005 Page(s):14 - 19

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