Explore the words cloud of the NETS project. It provides you a very rough idea of what is the project "NETS" about.
The following table provides information about the project.
Coordinator |
UNIVERSITY COLLEGE LONDON
Organization address contact info |
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 |
Take a look of project's partnership.
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1 | UNIVERSITY COLLEGE LONDON | UK (LONDON) | coordinator | 1˙587˙602.00 |
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.
year | authors and title | journal | last update |
---|---|---|---|
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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The information about "NETS" are provided by the European Opendata Portal: CORDIS opendata.