Comparing Network Sampling Methods
Session Number
Project ID: MATH 3
Advisor(s)
Dr. Lulu Kang; Illinois Institute of Technology
Discipline
Mathematics
Start Date
22-4-2020 10:25 AM
End Date
22-4-2020 10:40 AM
Abstract
Abstract: Networks can be used to analyze systems in the real world, however they are often too large for our computers to analyze within a reasonable amount of time. A solution to this is network sampling methods. These are just ways of sampling a smaller “representative” network that we can analyze. Being representative means that the sample retains certain characteristics of the original network. Because there are many characteristics, it means many different things for a sample network to be representative. I looked at three common sampling methods, being random degree node, random edge induced, and snowball sampling, and compared them based on how similar they were to an arbitrary original network for fundamental characteristics, degree and clustering coefficient.
Comparing Network Sampling Methods
Abstract: Networks can be used to analyze systems in the real world, however they are often too large for our computers to analyze within a reasonable amount of time. A solution to this is network sampling methods. These are just ways of sampling a smaller “representative” network that we can analyze. Being representative means that the sample retains certain characteristics of the original network. Because there are many characteristics, it means many different things for a sample network to be representative. I looked at three common sampling methods, being random degree node, random edge induced, and snowball sampling, and compared them based on how similar they were to an arbitrary original network for fundamental characteristics, degree and clustering coefficient.