The exchangeable graph model for statistical network analysis
Seminar Room 1, Newton Institute
Observations consisting of measurements on pairs of objects (or conditions) arise in a number of settings in the biological sciences (www.yeastgenome.org), with collections of scientific publications (www.jstor.org) and other hyper-linked resources (www.wikipedia.org), and in social networks (www.linkedin.com). Analyses of such data typically aim at identifying structure among the units of interest, in a low dimensional space, to support the generation of substantive hypotheses, to partially automate semantic categorization, to facilitate browsing, and to simplify complex data into useful patterns, more in general.
In this talk we introduce the exchangeable graph model and show its utility: 1. as a quantitative tool for exploring static/dynamic networks; 2. as a new paradigm for theoretical analyses of graph connectivity. Within this modeling context, we discuss alternative specifications and extensions that address fundamental issues in data analysis of complex interacting systems: bridging global and local phenomena, data integration, dynamics, and scalable inference.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.