TY - GEN
T1 - Parallel Planar Approximation for Large Networks
AU - Gasper, William
AU - Cooper, Kathryn
AU - Cornelius, Nathan
AU - Ali, Hesham
N1 - Funding Information:
This work was completed utilizing the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Planarity is an extensively studied topic within the graph theory domain. Planarity describes the potential for a graph to be embedded on a Euclidean plane without having any of its edges cross, and planarity is consequently a subject of particular interest in the context of graphs with nodes representing entities that are subject to physical constraints. While graph planarity has been extensively studied as a theoretical topic, it has also been successfully practically applied to architectural design [1] and circuit board design [2]. Our prior work found planarity to be a topic of interest in biological networks, specifically with regard to protein-protein interaction and domain-domain interaction networks. This work presents a novel parallel algorithm that produces planar approximations of large networks, which may be used to assess the relative planarity of large networks, for further analyses or ensemble methods, and visualization.
AB - Planarity is an extensively studied topic within the graph theory domain. Planarity describes the potential for a graph to be embedded on a Euclidean plane without having any of its edges cross, and planarity is consequently a subject of particular interest in the context of graphs with nodes representing entities that are subject to physical constraints. While graph planarity has been extensively studied as a theoretical topic, it has also been successfully practically applied to architectural design [1] and circuit board design [2]. Our prior work found planarity to be a topic of interest in biological networks, specifically with regard to protein-protein interaction and domain-domain interaction networks. This work presents a novel parallel algorithm that produces planar approximations of large networks, which may be used to assess the relative planarity of large networks, for further analyses or ensemble methods, and visualization.
KW - approximation algorithms
KW - biological networks
KW - graph theory
KW - parallel algorithms
UR - http://www.scopus.com/inward/record.url?scp=85125187943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125187943&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669815
DO - 10.1109/BIBM52615.2021.9669815
M3 - Conference contribution
AN - SCOPUS:85125187943
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 1948
EP - 1955
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -