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Analysis of large networks: structure and diffusion

Analysis of large networks: structure and diffusion. Jure Leskovec Machine Learning Department http ://www.cs.cmu.edu/~ jure. Networks. Today : large on-line systems leave detailed records of social activity On-line communities: MyScace , Facebook Email, blogging, instant messaging

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Analysis of large networks: structure and diffusion

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  1. Analysis of large networks:structure and diffusion Jure Leskovec Machine Learning Department http://www.cs.cmu.edu/~jure

  2. Networks • Today: large on-line systems leave detailed records of social activity • On-line communities: MyScace, Facebook • Email, blogging, instant messaging • On-line publications repositories, arXiv, MedLine • Emerging behavior (need lots of data): • Actions of individual nodes are practically independent but global patterns emerge

  3. Goals of my research • Statistical properties of large networks • Models that help understand these properties • Predict behavior of networked systems based on measured structural properties and local rules governing individual nodes

  4. Networks: Structure and Processes • What do I study in large networks? • The structure: • How do real networks look like? • How to generate a realistic network? • How to spot outliers and anomalies? • The processes and dynamics: • How do viruses, information, influence propagate in networks? • Who are the influential people, news sites? • How to select few influential nodes to maximize the impact?

  5. Research projects and results (1) • Models of network structure: • Given a real graph, we fit a model, and use this for anomaly detection, anonymization, prediction of network in the future • Social network of the whole world: • We analyzed the communication on MSN Instant Messenger network (240 million people, 1 billion conversations/day, 4.5TB compressed data) • Graph projections: • Just from the web graph (no content) we can reliably predict the quality of web search results

  6. Research projects and results (2) • Cascades in viral marketing: • 4 million people, 16 million recommendations • How do people recommend products and how do the recommendations propagate? • Information cascades: • Given a 50k of blogs over 1 year • How does information propagate? • Outbreak detection: • Where to place sensors in a network to detect epidemics quickly? • Who are the most influential blogs? S S

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