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Details of Grant 

EPSRC Reference: EP/X009505/1
Title: Statistical Network Models: Building the Foundation
Principal Investigator: Leng, Professor C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Statistics
Organisation: University of Warwick
Scheme: Standard Research - NR1
Starts: 01 August 2022 Ends: 31 July 2023 Value (£): 79,807
EPSRC Research Topic Classifications:
Non-linear Systems Mathematics Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 Mar 2022 EPSRC Mathematical Sciences Small Grants Panel March 2022 Announced
Summary on Grant Application Form
There are several reasons why someone may be reading this summary. It can be due to that this reader stumbles upon this summary by chance or that we share common research interests. Overall, it must be recognised that different individuals have different innate tendency to read scientific output in the first place, perhaps due to differences in curiosity, desire, or motivation. Understanding the mechanism on how social connections like this are formed, what affect their formation and to what extent has fascinated us for a very long time.

Statistical network analysis is a nascent discipline motivated by the need to describe various stochastic phenomena inherent in networks based on a probabilistic modelling perspective. This need has become increasingly pressing in recent years due to an exponential surge of networked data in a wide variety of fields including social media and complex systems. Though progress has been made on some particular models, a unifying theme is lacking with many foundational network models lacking statistical or inferential guarantees.

This project presents a first attempt in building the statistical foundation for the p1 model, a canonical model that can depict several major features of a network including sparsity (a network has relatively few edges in comparison to the total possible number of edges), homophily (similar nodes are more likely to connect than dissimilar ones), differential heterogeneity (nodes depict differential tendency to make connections), and reciprocity (connections tend to be mutual in directed networks). Proposed four decades ago, this model has catalysed several important statistical network models but remains poorly understood and studied.

This project aims to advance the research on the p1 model by investigating several open questions including the existence and the large-sample properties of its parameter estimates, as well as novel results for testing hypotheses of interest. Built on this advance, we will incorporate contextual information in the form of explanatory variables for explaining the forming of social ties. The project will serve as the springboard for a larger research programme in the future to develop a unified framework to deal with various dependency structures commonly seen in networks.
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Organisation Website: http://www.warwick.ac.uk