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NETS SIGNED

Networks in Time and Space

Total Cost €

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EC-Contrib. €

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Partnership

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Project "NETS" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY COLLEGE LONDON 

Organization address
address: GOWER STREET
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://www.homepages.ucl.ac.uk/
 Total cost 1˙587˙602 €
 EC max contribution 1˙587˙602 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-CoG
 Funding Scheme ERC-COG
 Starting year 2016
 Duration (year-month-day) from 2016-05-01   to  2021-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 1˙587˙602.00

Map

 Project objective

This research programme aims to develop a new theoretical framework for modelling and analysing spatio-temporal networks. The theory developed in this programme will underpin our ability to exactly specify the structured form of network behaviour in time and space. This will advance statistical methodology and theory, unifying results from stochastic processes with network theory to do so. New technical approaches to modelling will be proposed, as well as new asymptotic large sample scenarios. As a consequence of the methodological development, new analysis techniques for applications in real-world problems will be proposed that will improve our ability to make defensible conclusions from real data sets.

Modelling network data and estimating such models is challenging, especially in a modern setting, because most observed networks are very large. This leads to computational and inferential challenges. However, handling sparse and large networks is not enough to be able to describe highly structured network data. Most networks are coupled with secondary structure, and possess patterned behaviour in time and space. Linkages between nodes are frequently added and removed over time, and implicit structure is generated from latent spatial patterns.

The understanding of networks must be extended to encompass spatio-temporal patterns, to quantify such structural aspects of network data. This will require combining theory and methods from different parts of mathematics, and developing new statistical theory. This project therefore aims to a) model temporally evolving networks, b) understand the characteristics of growing and decaying networks, c) model and estimate spatial and temporal characteristics in networks and d) propose new models of spatial structure. These developments will combine to form a new theoretical framework for families of networks with a rich and complex structure.

 Publications

year authors and title journal last update
List of publications.
2018 Tuomas Rajala, Sofia Charlotta Olhede, David John Murrell
When do we have the power to detect biological interactions in spatial point patterns?
published pages: 1-11, ISSN: 0022-0477, DOI: 10.1111/1365-2745.13080
Journal of Ecology 2019-10-09
2018 S. C. Olhede, P. J. Wolfe
The growing ubiquity of algorithms in society: implications, impacts and innovations
published pages: 20170364, ISSN: 1364-503X, DOI: 10.1098/rsta.2017.0364
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376/2128 2019-10-09
2017 Jonathan M. Lilly, Adam M. Sykulski, Jeffrey J. Early, Sofia C. Olhede
Fractional Brownian motion, the Matérn process, and stochastic modeling of turbulent dispersion
published pages: 481-514, ISSN: 1607-7946, DOI: 10.5194/npg-24-481-2017
Nonlinear Processes in Geophysics 24/3 2019-10-09
2018 Adam M. Sykulski, Sofia C. Olhede, Arthur P. Guillaumin, Jonathan M. Lilly, Jeffrey J. Early
The De-Biased Whittle Likelihood
published pages: , ISSN: 0006-3444, DOI:
Biometrika 2019-10-09
2016 Adam M. Sykulski, Sofia C. Olhede, Jonathan M. Lilly
A Widely Linear Complex Autoregressive Process of Order One
published pages: 6200-6210, ISSN: 1053-587X, DOI: 10.1109/TSP.2016.2599503
IEEE Transactions on Signal Processing 64/23 2019-06-18
2017 Adam M. Sykulski, Sofia Charlotta Olhede, Jonathan M. Lilly, Jeffrey J. Early
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals
published pages: 3136-3151, ISSN: 1053-587X, DOI: 10.1109/TSP.2017.2686334
IEEE Transactions on Signal Processing 65/12 2019-06-18
2017 PA Maugis CE Priebe S. C. Olhede P. J. Wolfe
Statistical inference for network samples using subgraph counts
published pages: , ISSN: , DOI:
2019-06-18
2017 PA Maugis PJ Wolfe SC Olhede
Fast counting of medium-sized rooted subgraphs
published pages: , ISSN: , DOI:
2019-06-18
2017 PA Maugis SC Olhede PJ Wolfe
Topology reveals universal features for network comparison
published pages: , ISSN: , DOI:
2019-06-18
2018 T. Rajala, D. J. Murrell, S. C. Olhede
Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection
published pages: 1237-1273, ISSN: 0035-9254, DOI: 10.1111/rssc.12281
Journal of the Royal Statistical Society: Series C (Applied Statistics) 67/5 2019-06-18
2017 Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede, Jeffrey J. Early, Jonathan M. Lilly
Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements
published pages: 668-710, ISSN: 0143-9782, DOI: 10.1111/jtsa.12244
Journal of Time Series Analysis 38/5 2019-06-18

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