As an example, we consider a covering problem which arises naturally for recruiters on social networks, and show how small differences between the information on neighbors in LinkedIn and Facebook lead to large differences in their utility to recruiters. In the second part of the talk, I consider problems where there is no a priori limit on data access.
Nevertheless, we show how for a host of questions - pagerank, coverage, diffusion, determining the most influential nodes in the independent cascade model - we can exploit locality to design highly efficient algorithms, often with quasilinear or sublinear runtimes. Chayes joined Microsoft Research in , when she co-founded the Theory Group. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, and algorithmic game theory.
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She is the co-author of over scientific papers and the co-inventor of more than 25 patents. Network data and more generally relational data encoding the pairwise relations between objects appear in many fields. For instance in biology, a protein network connects interacting partners, while in a social network, links among people indicate social relationships. The problems of analysing, understanding and modelling such networks have attracted interest from many research communities.
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I will briefly review some probabilistic approaches to modelling networks. The key idea behind many such models is that each object has certain latent features, and that observed links in the network depend on these latent features. Probabilistic inference allows one to discover the potentially unbounded number of latent features including discovering communities as a special case , predict missing links, and generally learn about the statistical properties of the networks.
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Many of these models can be cast within the theoretical framework of exchangeable arrays established by Aldous, Hoover and Kallenberg. I will describe our work on a general network model the Random Function Model that instantiates this theory using Gaussian processes, and relate it to existing models. I will also discuss our work on the Infinite Latent Attribute ILA model which allows for a highly structured nonparametric latent variable representation of nodes in a network.
Finally, I will describe our Latent Feature Propagation model for dynamic networks. What ties these models together is the idea that rich latent representations underlie the structure of networks, and that these can be discovered via Bayesian inference. Joint work with Creighton Heaukulani, David A.
His work has included research on human sensorimotor control, cognitive science, statistics, and machine learning.
His current focus is on Bayesian approaches to statistical machine learning, and has recently started a research programme on machine learning applications to information retrieval. The Structure of Online Diffusion Abstract: Viral products and ideas are intuitively understood to grow through a person-to-person diffusion process analogous to that of an infectious disease. Until recently, however, it has been prohibitively difficult to directly observe such viral events, and thus to rigorously quantify or characterize their structural properties.
We use this notion of structural virality to analyze a unique dataset of several billion diffusion eventsincluding the propagation of news stories, videos, images, and petitionson a large online social network. We find that the very largest observed events nearly always exhibit high structural virality, providing some of the first direct evidence that many of the most popular products and ideas grow through person-to-person diffusion.
However, medium-sized eventshaving thousands of adoptersexhibit surprising structural diversity, and are as likely to grow via broadcast as through viral means. This unique text is a must-read, must-use tool for software developers, researchers and graduate students working on multimedia, web search, data mining and machine learning, and related disciplines. High-level managers and professional engineers interested in emerging social media modeling and computing technologies will also find the book to be an invaluable reference.
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Social Computing | Know-Center
Here's how restrictions apply. Review From the reviews: Presents contributions from an international selection of preeminent experts in the field Discusses topics on social-media content analysis, including social-image tag analysis and ranking, tag-based social image search, analysis by combining multimodal features, and multi-label social-image annotation Examines social-media system design and analysis, covering mechanisms for incentivizing contributions, analysis of users and online behaviors in video sharing portals, and visual analytic tools for event analysis Investigates access control for privacy and security issues in social networks Describes emerging applications of social media, for music recommendation, automatic image annotation, and the analysis and improvement of photo-books This unique text is a must-read, must-use tool for software developers, researchers and graduate students working on multimedia, web search, data mining and machine learning, and related disciplines.
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