Efficient methods and readily customizable libraries for managing complexity of large networks.

BACKGROUND:One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a "hairball" network, hindering effective analysis. One ex...

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Main Authors: Ugur Dogrusoz, Alper Karacelik, Ilkin Safarli, Hasan Balci, Leonard Dervishi, Metin Can Siper
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5973603?pdf=render
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spelling doaj-e7ee27c9c44b41aaa9502d85d793805c2020-11-25T02:12:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01135e019723810.1371/journal.pone.0197238Efficient methods and readily customizable libraries for managing complexity of large networks.Ugur DogrusozAlper KaracelikIlkin SafarliHasan BalciLeonard DervishiMetin Can SiperBACKGROUND:One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a "hairball" network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis. Another such method is hiding currently unnecessary details, to later gradually reveal on demand. Major challenges when applying complexity reduction operations on large networks include efficiency and maintaining the user's mental map of the drawing. RESULTS:We developed specialized incremental layout methods for preserving a user's mental map while managing complexity of large networks through expand-collapse and hide-show operations. We also developed open-source JavaScript libraries as plug-ins to the web based graph visualization library named Cytsocape.js to implement these methods as complexity management operations. Through efficient specialized algorithms provided by these extensions, one can collapse or hide desired parts of a network, yielding potentially much smaller networks, making them more suitable for interactive visual analysis. CONCLUSION:This work fills an important gap by making efficient implementations of some already known complexity management techniques freely available to tool developers through a couple of open source, customizable software libraries, and by introducing some heuristics which can be applied upon such complexity management techniques to ensure preserving mental map of users.http://europepmc.org/articles/PMC5973603?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Ugur Dogrusoz
Alper Karacelik
Ilkin Safarli
Hasan Balci
Leonard Dervishi
Metin Can Siper
spellingShingle Ugur Dogrusoz
Alper Karacelik
Ilkin Safarli
Hasan Balci
Leonard Dervishi
Metin Can Siper
Efficient methods and readily customizable libraries for managing complexity of large networks.
PLoS ONE
author_facet Ugur Dogrusoz
Alper Karacelik
Ilkin Safarli
Hasan Balci
Leonard Dervishi
Metin Can Siper
author_sort Ugur Dogrusoz
title Efficient methods and readily customizable libraries for managing complexity of large networks.
title_short Efficient methods and readily customizable libraries for managing complexity of large networks.
title_full Efficient methods and readily customizable libraries for managing complexity of large networks.
title_fullStr Efficient methods and readily customizable libraries for managing complexity of large networks.
title_full_unstemmed Efficient methods and readily customizable libraries for managing complexity of large networks.
title_sort efficient methods and readily customizable libraries for managing complexity of large networks.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2018-01-01
description BACKGROUND:One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a "hairball" network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis. Another such method is hiding currently unnecessary details, to later gradually reveal on demand. Major challenges when applying complexity reduction operations on large networks include efficiency and maintaining the user's mental map of the drawing. RESULTS:We developed specialized incremental layout methods for preserving a user's mental map while managing complexity of large networks through expand-collapse and hide-show operations. We also developed open-source JavaScript libraries as plug-ins to the web based graph visualization library named Cytsocape.js to implement these methods as complexity management operations. Through efficient specialized algorithms provided by these extensions, one can collapse or hide desired parts of a network, yielding potentially much smaller networks, making them more suitable for interactive visual analysis. CONCLUSION:This work fills an important gap by making efficient implementations of some already known complexity management techniques freely available to tool developers through a couple of open source, customizable software libraries, and by introducing some heuristics which can be applied upon such complexity management techniques to ensure preserving mental map of users.
url http://europepmc.org/articles/PMC5973603?pdf=render
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