Network Kriging - Predicting the Attributes of Nodes in a Network

This thesis develops a method which predicts the role of a node in a social network. For illustrative purposes the network used is a subset of Al-Qaeda from 1998 which contains a total of 160 members.While doing exploratory analysis on this network we noticed that there seemed to be an underlying connection with the distance between two members and their roles. This led to developing a prediction method which could exploit this correlation structure. We use the geostatistical prediction method called Kriging that is modified to preform in a network; which we call Network Kriging.This thesis gives the background knowledge necessary to understand the techniques, shows the results of Network Kriging and compares results to those using the K-Nearest Neighbours algorithm. We found that for important roles, such as Emir (Leadership), Network Kriging performs better than K-Nearest Neighbours.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Probability and Statistics

Grantor dc:publisher

Carleton University

Year dc:date.issued

2016

Author dc:creator

• Hockey, Daniel

Statement dc:rights

• Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2016-11356

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/39863