APS March Meeting 2016
Volume 61, Number 2
Monday–Friday, March 14–18, 2016;
Baltimore, Maryland
Session Y12: Inference in Complex Networks
11:15 AM–2:15 PM,
Friday, March 18, 2016
Room: 308
Sponsoring
Unit:
GSNP
Chair: Adilson Motter, Northwestern Unviersity
Abstract ID: BAPS.2016.MAR.Y12.2
Abstract: Y12.00002 : Nonparametric inference of network structure and dynamics
11:51 AM–12:27 PM
Preview Abstract
Abstract
Author:
Tiago P. Peixoto
(University of Bremen)
The network structure of complex systems determine their function and
serve as evidence for the evolutionary mechanisms that lie behind
them. Despite considerable effort in recent years, it remains an open
challenge to formulate general descriptions of the large-scale structure
of network systems, and how to reliably extract such information from
data. Although many approaches have been proposed, few methods attempt
to gauge the statistical significance of the uncovered structures, and
hence the majority cannot reliably separate actual structure from
stochastic fluctuations. Due to the sheer size and high-dimensionality
of many networks, this represents a major limitation that prevents
meaningful interpretations of the results obtained with such
nonstatistical methods.
In this talk, I will show how these issues can be tackled in a
principled and efficient fashion by formulating appropriate generative
models of network structure that can have their parameters inferred from
data. By employing a Bayesian description of such models, the inference
can be performed in a nonparametric fashion, that does not require any
\emph{a priori} knowledge or \emph{ad hoc} assumptions about the data. I
will show how this approach can be used to perform model comparison, and
how hierarchical models yield the most appropriate trade-off between
model complexity and quality of fit based on the statistical evidence
present in the data. I will also show how this general approach can be
elegantly extended to networks with edge attributes, that are embedded
in latent spaces, and that change in time. The latter is obtained via a
fully dynamic generative network model, based on arbitrary-order Markov
chains, that can also be inferred in a nonparametric fashion.
Throughout the talk I will illustrate the application of the methods
with many empirical networks such as the internet at the autonomous
systems level, the global airport network, the network of actors and
films, social networks, citations among websites, voting correlations
among politicians, co-occurrence of disease-causing genes and others.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2016.MAR.Y12.2