01-intro_annot

01-intro_annot - CS224W:SocialandInformationNetworkAnalysis

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CS224W: Social and Information Network Analysis Jure Leskovec, Stanford University http://cs224w.stanford.edu
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hy is the role of networks in CS Info Why is the role of networks in CS, Info science, Social science, (Physics, Economics, iology) expanding? Biology) expanding? More data ise of the Web and So ial media Rise of the Web and Social media Shared vocabulary between (very different fields) 9/20/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 2
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ow do we reason about networks How do we reason about networks Empirical: look at large networks and see what you find athematical models robabilistic graph theory Mathematical models: probabilistic, graph theory Algorithms for analyzing graphs hat do e hope to achie e from models of What do we hope to achieve from models of networks? Patterns and statistical properties of network data Design principles and models Understand why networks are organized the way they re (predict behavior of networked systems) are (predict behavior of networked systems) 9/20/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 3
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etwork data is increasingly available: Network data is increasingly available: L arge on line computing applications where data can naturally be represented as a network : On line communities: Facebook (120 million users) Communication: Instant Messenger (~1 billion users) News and Social media: Blogging (250 million users) Also in systems biology, health, medicine, … etwork is a set of weakly interacting entities Network is a set of weakly interacting entities Links give added value: oogle realized web ages are connected Google realized web pages are connected Collective classification 9/20/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 4
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troduce roperties models d ols r Introduce properties, models and tools for large real world networks iffusion processes in networks diffusion processes in networks through real mining applications Goal: find patterns, rules, clusters, outliers, … large static and evolving graphs in large static and evolving graphs In processes spreading over the networks 9/20/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 5
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) ) (b) (c) (a) (d) (e) Internet (a) itation etwork (b) Sexual network (d) Citation network (b) World Wide Web (c) 9/20/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 6 Dating network (e)
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Information networks: orld Wide Web: World Wide Web: hyperlinks Citation networks Blog networks cial networks Social networks: Organizational networks Communication networks Collaboration networks Florentine families Web graph Sexual networks Collaboration networks Technological networks: ower grid Power grid Airline, road, river networks Telephone networks ternet Internet Autonomous systems 9/20/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 7 Collaboration network Friendship network
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01-intro_annot - CS224W:SocialandInformationNetworkAnalysis

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