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An efficient parallel algorithm for two-layer planarization in graph drawing

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5 Author(s)
Xin Shin Xu ; Sch. of Comput. Sci. & Technol., Shandong Univ., China ; Zheng Tang ; Rong Long Wang ; Qi Ping Cao
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We present a parallel algorithm for the two-layer planarization problem using a gradient ascent learning of Hopfield network. This algorithm which is designed to embed a two-layer graph on a plane, uses the Hopfield network to get a near-maximal planar subgraph, and increases the energy by modifying weights in a gradient ascent direction to help the Hopfield network escape from the state of near-maximal planar subgraph to the state of the maximal planar subgraph. The experimental results show that the proposed algorithm can generate better solutions than the traditional Hopfield network.

Published in:

TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering  (Volume:3 )

Date of Conference:

28-31 Oct. 2002

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