Evaluation of rumor source estimation algorithms

In modern society, social network had evolved as a powerful tool for users to interact with others all over the world. As time goes by, many social network platforms with huge number of users such as Weibo, Facebook and Twitter have become new means of rumor-spreading platforms. Detecting the rumor...

Full description

Saved in:
Bibliographic Details
Main Author: Xie, BinBin
Other Authors: Tay Wee Peng
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/75392
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-75392
record_format dspace
spelling sg-ntu-dr.10356-753922023-07-07T16:33:08Z Evaluation of rumor source estimation algorithms Xie, BinBin Tay Wee Peng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In modern society, social network had evolved as a powerful tool for users to interact with others all over the world. As time goes by, many social network platforms with huge number of users such as Weibo, Facebook and Twitter have become new means of rumor-spreading platforms. Detecting the rumor source on social network is essential as the rumors constantly cause harmful effects to the public wellness as well as human in terms of social exposure, physical and psychological well-being. To detect the rumor source, many techniques have been proposed in recent years. The performance of the techniques should be evaluated to examine their effectiveness of detecting the rumor source. In this project, Belief Propagation (BP) algorithm was selected to achieve our objectives. BP algorithm is a decoding algorithm based on passing messages between local functions and the corresponding variables and computes the marginal probability distribution of the true source. We operated the algorithm on factor graph under SIR model and tested it on both random regular graphs (RRG) and Erdős Rényi (ER) Graph on synthetic datasets. We evaluated the performance of the algorithm based on an indicator called normalized rank of true source. The simulation results showed the BP algorithm can effectively estimate the rumor source in terms of the small epidemic size and observation time. Bachelor of Engineering 2018-05-31T03:14:44Z 2018-05-31T03:14:44Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75392 en Nanyang Technological University 62 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Xie, BinBin
Evaluation of rumor source estimation algorithms
description In modern society, social network had evolved as a powerful tool for users to interact with others all over the world. As time goes by, many social network platforms with huge number of users such as Weibo, Facebook and Twitter have become new means of rumor-spreading platforms. Detecting the rumor source on social network is essential as the rumors constantly cause harmful effects to the public wellness as well as human in terms of social exposure, physical and psychological well-being. To detect the rumor source, many techniques have been proposed in recent years. The performance of the techniques should be evaluated to examine their effectiveness of detecting the rumor source. In this project, Belief Propagation (BP) algorithm was selected to achieve our objectives. BP algorithm is a decoding algorithm based on passing messages between local functions and the corresponding variables and computes the marginal probability distribution of the true source. We operated the algorithm on factor graph under SIR model and tested it on both random regular graphs (RRG) and Erdős Rényi (ER) Graph on synthetic datasets. We evaluated the performance of the algorithm based on an indicator called normalized rank of true source. The simulation results showed the BP algorithm can effectively estimate the rumor source in terms of the small epidemic size and observation time.
author2 Tay Wee Peng
author_facet Tay Wee Peng
Xie, BinBin
format Final Year Project
author Xie, BinBin
author_sort Xie, BinBin
title Evaluation of rumor source estimation algorithms
title_short Evaluation of rumor source estimation algorithms
title_full Evaluation of rumor source estimation algorithms
title_fullStr Evaluation of rumor source estimation algorithms
title_full_unstemmed Evaluation of rumor source estimation algorithms
title_sort evaluation of rumor source estimation algorithms
publishDate 2018
url http://hdl.handle.net/10356/75392
_version_ 1772828583967850496