Bayesian attributed network sampling

We address the problem of sampling in attributed networks. While uniform sampling is a task independent sampling method, in real-world, this is often difficult to implement as it requires random access to all the nodes of graph. Link tracing sampling methods such as random Walk, expansion sampling overcome this problem, however they do not utilize the information provided by attributes of the nodes and just use the topology of the graph. We propose a network sampling method which is task independent and utilizes the node attributes. Our approach is based on introducing maximum unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Kumar, Ankit

Contributors dc:contributor

• Sundaram, Hari

• Sampling
• Data Mining
• Social and Information Networks
• Bayesian Analysis

Statement dc:rights

• Copyright 2020 Ankit Kumar

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108049

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108049