Files in this item
Files | Description | Format |
---|---|---|
application/pdf ![]() | (no description provided) |
Description
Title: | Bayesian attributed network sampling |
Author(s): | Kumar, Ankit |
Advisor(s): | Sundaram, Hari |
Department / Program: | Computer Science |
Discipline: | Computer Science |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | M.S. |
Genre: | Thesis |
Subject(s): | Sampling
Data Mining Social and Information Networks Bayesian Analysis |
Abstract: | 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. |
Issue Date: | 2020-05-13 |
Type: | Thesis |
URI: | http://hdl.handle.net/2142/108049 |
Rights Information: | Copyright 2020 Ankit Kumar |
Date Available in IDEALS: | 2020-08-26 |
Date Deposited: | 2020-05 |
This item appears in the following Collection(s)
-
Dissertations and Theses - Computer Science
Dissertations and Theses from the Dept. of Computer Science -
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois