Clearing Contamination in Large Networks
In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching proces...
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ndltd-uvic.ca-oai-dspace.library.uvic.ca-1828-56362015-01-29T16:52:41Z Clearing Contamination in Large Networks Simpson, Michael Thomo, Alex Srinivasan, Venkatesh Social Networks Graph Searching Approximation Algorithms In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions and Twitter. The experiments reveal that in most cases our algorithm performs near optimally. Graduate 2014-08-29T20:10:37Z 2014-08-29T20:10:37Z 2014 2014-08-29 Thesis http://hdl.handle.net/1828/5636 English en Available to the World Wide Web http://creativecommons.org/publicdomain/zero/1.0/ |
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English en |
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Social Networks Graph Searching Approximation Algorithms |
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Social Networks Graph Searching Approximation Algorithms Simpson, Michael Clearing Contamination in Large Networks |
description |
In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions and Twitter. The experiments reveal that in most cases our algorithm performs near optimally. === Graduate |
author2 |
Thomo, Alex |
author_facet |
Thomo, Alex Simpson, Michael |
author |
Simpson, Michael |
author_sort |
Simpson, Michael |
title |
Clearing Contamination in Large Networks |
title_short |
Clearing Contamination in Large Networks |
title_full |
Clearing Contamination in Large Networks |
title_fullStr |
Clearing Contamination in Large Networks |
title_full_unstemmed |
Clearing Contamination in Large Networks |
title_sort |
clearing contamination in large networks |
publishDate |
2014 |
url |
http://hdl.handle.net/1828/5636 |
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AT simpsonmichael clearingcontaminationinlargenetworks |
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1716729718951116800 |