SINM 2015 - Statistical Inference for Network Models symposium 2015

The Statistical Inference for Network Models symposium is a satellite of the NetSci2015 conference, to be held June 1, 2015 in Zaragoza, Spain. It will feature a mix of invited and contributed talks, which you can view on the Symposium Schedule. All attendees of this symposium must be registered for the NetSci2015 conference in order to attend. Please note the important dates and deadlines on the right.
Call for Abstracts
We invite abstracts of new and/or previously published work for contributed talks to take place at the symposium. We hope for a broad range of topics to be covered, across theory, methodology, and application to empirical network data. Potential topics include:
Generative models for network structure
Community structure, hierarchical structure, block modeling
Model selection, comparison, and validation
Efficient algorithms
Intersections between statistical physics and machine learning
Detectability limits
Network comparison
Prediction and anomaly detection
Statistical relational learning
Bayesian nonparametrics
Graphon estimation
Interfaces with spectral methods
Social networks and social media
Biological networks
Model-based knowledge discovery
New domains of application
New models for applied problems
The deadline for abstract submission is April 10, 2015, and acceptance notifications will be sent April 17, 2015. A more detailed description follows.
Abstract Instructions
Abstract submission will be handled by EasyChair and is free of charge. There is no word limit on abstracts but please limit abstract length to one page, including title, authors, equations, and up to one expository figure. All abstracts will be considered for contributed talks; there will be no posters for SINM 2015.
Submit an abstract here
Symposium Description
This workshop will address the intersection of two trends in network science. On the one hand, real-world networks are increasingly annotated with rich metadata, including vertex or edge attributes, temporal information, and more. Making sense of such data requires moving beyond simple models of network structure. On the other, hypotheses about network structure and the processes that create those patterns are increasingly sophisticated. The tools of statistical inference for network models offer a principled and effective approach for both understanding richly annotated network data and testing interesting network hypotheses.
In particular, probabilistic models are a quantitative approach that allows researchers both to infer complicated hidden structural patterns in existing data and to generate synthetic data sets whose structure is statistically similar to real data. These models facilitate handling many of the challenges of understanding real data, including controlling for noise and missing values, and they connect theory with data by providing interpretable results. Statistical inference is thus a powerful and useful tool for modeling and understanding networks.
The development of new tools and their application to understand real systems is now a major community effort in network science. Despite their power and utility, however, these techniques are not as easy or approachable as simpler tools, like degree distributions, centrality scores, and clustering coefficients. Increasingly, new applications and richer data sets offer new opportunities for developing and applying the principled techniques of statistical inference to networks.
This satellite symposium will build on a successful first satellite at NetSci2014, by uniting theoretical and applied researchers, and bringing together approaches from across network science, including machine learning, statistics, and physics. This broad cross-section of disciplines shares problems and even approaches, but each discipline brings a different perspective, emphasis and vocabulary. The purpose of this symposium is to provide a platform for cross-pollination of ideas and to reveal that the diversity of approaches to a common set of problems is a strength.
Invited Speakers - Abstracts and Titles
Ceren Budak, Microsoft Research
JP Onnela, Harvard
Patrick Wolfe, University College London
Dena Asta, Carnegie Mellon
Organizers
Abigail Jacobs, Colorado
Leto Peel, Colorado
Dan Larremore, Harvard T.H. Chan School of Public Health
Aaron Clauset, Colorado