Influence maximization on dynamic graphs

In the real world, there are many applications of the stream such as social networks and telecommunication networks. Due to the great potential in targeted recommendations and viral marketing, there is a tremendous incentive to find the top k influencers. However, due to the nature of the dynamic gr...

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Main Author: Lim, Zi Xiang
Other Authors: Arijit Khan
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/70093
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-700932023-03-03T20:54:34Z Influence maximization on dynamic graphs Lim, Zi Xiang Arijit Khan School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering In the real world, there are many applications of the stream such as social networks and telecommunication networks. Due to the great potential in targeted recommendations and viral marketing, there is a tremendous incentive to find the top k influencers. However, due to the nature of the dynamic graph, we will tackle the inherent problem of finding the top k influencers as the dynamic graph is constantly changing. We model the stream as slip shots of the static graph that changes over the time. From there, we take note of the incoming changes to the edges and attempt in handling those mutually exclusive cases. Under the popular use of the independent cascade model, we develop an algorithm that performs an update to the top k nodes based on the mutually exclusive cases of edge addition, edge deletion and edge modification. Using up to date research by other paper and the DBLP collaboration network dataset, we validate the use of our method on the network and present a timeline of the most influential authors in the network. Bachelor of Engineering (Computer Science) 2017-04-11T05:17:28Z 2017-04-11T05:17:28Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70093 en Nanyang Technological University 46 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::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Lim, Zi Xiang
Influence maximization on dynamic graphs
description In the real world, there are many applications of the stream such as social networks and telecommunication networks. Due to the great potential in targeted recommendations and viral marketing, there is a tremendous incentive to find the top k influencers. However, due to the nature of the dynamic graph, we will tackle the inherent problem of finding the top k influencers as the dynamic graph is constantly changing. We model the stream as slip shots of the static graph that changes over the time. From there, we take note of the incoming changes to the edges and attempt in handling those mutually exclusive cases. Under the popular use of the independent cascade model, we develop an algorithm that performs an update to the top k nodes based on the mutually exclusive cases of edge addition, edge deletion and edge modification. Using up to date research by other paper and the DBLP collaboration network dataset, we validate the use of our method on the network and present a timeline of the most influential authors in the network.
author2 Arijit Khan
author_facet Arijit Khan
Lim, Zi Xiang
format Final Year Project
author Lim, Zi Xiang
author_sort Lim, Zi Xiang
title Influence maximization on dynamic graphs
title_short Influence maximization on dynamic graphs
title_full Influence maximization on dynamic graphs
title_fullStr Influence maximization on dynamic graphs
title_full_unstemmed Influence maximization on dynamic graphs
title_sort influence maximization on dynamic graphs
publishDate 2017
url http://hdl.handle.net/10356/70093
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