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? 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

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.

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

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

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

First Network Model on the Small-world Phenomenon

Strong Link V.S. Weak Link Bob Mary

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

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

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.

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.

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

Degree in social networks

Density in social networks

Centrality in social networks Degree Centrality Closeness Centrality Betweeness Centrality

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

Matthew Effect The rich get richer and the poor get poorer

Internet Structure

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)

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

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

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

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

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

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

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.

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?

Infectious Disease Protection (cont)

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

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

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

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

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

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

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)

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