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Social Networks and Related Applications

李漢銘李漢銘臺灣科技大學資訊工程系臺灣科技大學資訊工程系中央研究院資訊科學研究所中央研究院資訊科學研究所

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Outline

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

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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

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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.

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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

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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. Stro

gatz)

• 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

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Six Degrees of Separation

• 1967: Small World Phenomenon (Stanley Milgram)

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First Network Model on the Small-world Phenomenon

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Strong Link V.S. Weak Link

Mary

Bob

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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

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Friendster

• An online community that connects people through networks of friends for dating or making new friends

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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.

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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 doesnot connect to A.

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How to do social network analysis

There are three key principles in social networks.– Degree

– Density

– Centrality

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Degree in social networks

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Density in social networks

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Centrality in social networks

• Degree Centrality

• Closeness Centrality

• Betweeness Centrality

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Related applications

• Matthew Effect

• Internet Structure

• Anti-Spam

• Infectious Disease Protection

• Motif Finding

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Matthew Effect

• The rich get richer and the poor get poorer

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Internet Structure

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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)

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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

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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

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Honey Pot Statistics of Spam

Data Source: http://www.projecthoneypot.org/

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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

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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

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Personal E-mail Networks

.In the largest component , none of nodes

share neighbors

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Personal E-mail Networks (cont)

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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.

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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?

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Infectious Disease Protection (cont)Infectious Disease Protection (cont)

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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

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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

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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

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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

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Related resources (cont)

• [book] Mark Buchanan,NEXUS:small worlds and the groundbreaking science of networks. 中文譯本 : 連結

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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

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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)

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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