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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 1 Outline • • • • • • • • What is a social network Why social networks History of social networks Social network analysis Related applications Related resources Related keywords References 2 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 3 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. 4 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 5 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 6 Six Degrees of Separation • 1967: Small World Phenomenon (Stanley Milgram) 7 First Network Model on the Small-world Phenomenon 8 Strong Link V.S. Weak Link Bob Mary 9 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 10 Friendster • An online community that connects people through networks of friends for dating or making new friends 11 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. 12 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. 13 How to do social network analysis There are three key principles in social networks. – Degree – Density – Centrality 14 Degree in social networks 15 Density in social networks 16 Centrality in social networks • Degree Centrality • Closeness Centrality • Betweeness Centrality 17 Related applications • • • • • Matthew Effect Internet Structure Anti-Spam Infectious Disease Protection Motif Finding 18 Matthew Effect • The rich get richer and the poor get poorer 19 Internet Structure 20 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) 21 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 22 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 23 Honey Pot Statistics of Spam Data Source: http://www.projecthoneypot.org/ 24 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 25 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 26 Personal E-mail Networks . In the largest component , none of nodes share neighbors 27 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. 28 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? 29 Infectious Disease Protection (cont) 30 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 31 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 32 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 33 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 34 Related resources (cont) • [book] Mark Buchanan,NEXUS:small worlds and the groundbreaking science of networks. 中文譯本:連結 35 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 36 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,1,6067(1967) 37 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 38