Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

In the current landscape of "Big Data", large networks (graphs) are pervasive objects and have attracted much interest. A (social) network is a complex relational graph consisting of nodes (actors) and edges (relations), where both nodes and edges may have a number of attributes (covariates) associated with them. Examples include social networks such as Facebook, citation and collaboration networks (such as arXiv), web graphs (such as Wikipedia) and communication networks. Researchers are interested in modelling network structures and relations, as well as how these depend on the node and edge attributes; and, possibly, how they evolve over time. The investigation and analysis of these large networks can prove difficult due to the sheer number of nodes and edges and associated attribute data. Another difficulty that researchers are faced with is the prospect of having only a single observed network from which estimates are obtained. The general objectives for this research are to investigate efficient sampling methods on networks and to investigate both parametric and nonparametric inference for network models based on samples using both frequentist and Bayesian methods.
Specific program objectives include research on specific sampling techniques, such as ranked based sampling techniques and resampling techniques, for making inferences about large networks when analyzing the whole network is not computationally feasible. Through this research, I plan to train at least three M.Sc. students and two Ph.D. students as well as fixe undergraduate students.