Graph Sampling

Graph Sampling

Graph Sampling

Instructor: Li-Chun Zhang

July 14, 2023

This conference is currently not open for registrations or submissions.

About the Short Course: 1 Day Course

Many technological, socio-economic, biological phenomena exhibit a graph structure that may be the central interest of study, or the edges may provide effectively access to those nodes that are the primary targets. Either way, graph sampling is a statistical approach to study real graphs, which is universally applicable based on exploring the variation over all possible subgraphs that can be taken from the given population graph, according to a specified method of sampling, irrespectively of the unknown properties of the target population graph.

Graph sampling encompasses the established theory of finite population sampling, because any latter situation can be represented as a special case of the former. All the so-called “unconventional” finite population sampling techniques, such as indirect, network, adaptive cluster or line-intercept sampling, can be more effectively studied as special cases of graph sampling. Whereas snowball sampling and various random walk sampling yield respectively breadth-first or depth-first observations in genuine graph problems. The course provides an introduction to the central concepts of graph sampling, the most common sampling methods, and the construction of graph sampling strategy. A wide range of application areas are illustrated, such as epidemiological studies, environmental and spatial statistics, network function learning, graph embedding.

In-Person Event. Location Of Short Courses: University of Ottawa

Who is this course for?

Graduate students, statisticians working with sampling methods, data scientists interested in network analysis, graph mining or compression.

Level Of Instruction: Intermediate

Learning Outcomes

Understanding the central concepts of graph sampling and estimation Strategies.
Estimation of graph parameters by breadth-first snowball sampling, which is based on the known sampling probabilities.
Depth-first random walk sampling from arbitrary simple graphs with desired stationary probabilities and adjustable chain mixing speed
Illustration of a wide range of application areas and topics

Course Materials

Zhang, L.-C. (2022). Graph Sampling. Chapman and Hall/CRC.

Delivery Structure

Lectures, Q&A

Knowledge Assumed

Finite population sampling theory
Basic probability calculus
Basics of graph, discrete mathematics

About the instructor:  Li-Chun Zhang

Li-Chun Zhang is Professor of Social Statistics at the University of Southampton, Senior Researcher at Statistics Norway. He has researched and published on topics such as finite population sampling design and coordination, graph sampling, machine learning, sample survey estimation, non-response, measurement errors, small area estimation, index number calculations, editing and imputation, register-based statistics, population size estimation, statistical matching, record linkage.

Affiliations: University of Southampton, UK; Statistics Norway, Norway

This conference is currently not open for registrations or submissions.