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

Publishing date: 2018-05-29

Published on: PLOS ONE

summary: 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. Ugur Dogrusoz and colleagues have 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. They have also 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.

authors: Ugur Dogrusoz, Alper Karacelik, Ilkin Safarli, Hasan Balci, Leonard Dervishi, Metin Can Siper

link to paper: 10.1371/journal.pone.0197238

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