Connectionists: NetSci 2015, Symposium on Statistical Inference for Network Models

[Aaron Clauset]

Call for papers - Statistical Inference for Network Models 2015

***The deadline for abstract submission is April 10, 2015***

http://danlarremore.com/sinm2015/

The Statistical Inference for Network Models symposium is a satellite of the International Conference on Network Science (NetSci) 2015, held June 1, 2015 in Zaragoza, Spain this year. 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


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
===================
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
Aaron Clauset, Colorado

------------------------------------

Aaron Clauset
Assistant Professor
Computer Science Department,
University of Colorado, Boulder, and
External Faculty, Santa Fe Institute
http://structureandstrangeness.com/
aaron.clauset at colorado.edu
@aaronclauset