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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


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