Open AccessEngineering Bi-Connected Component Overlay for Maximum-Flow Parallel Acceleration in Large Sparse Graph
Author(s) -
Yang Liu,
Wei Wei,
Heyang Xu
Publication year - 2018
Publication title -
xibei gongye daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.158
H-Index - 17
eISSN - 2609-7125
pISSN - 1000-2758
DOI - 10.1051/jnwpu/20183650955
Subject(s) - maximum flow problem , computer science , computation , graph , preprocessor , acceleration , graph bandwidth , connected component , flow network , algorithm , theoretical computer science , line graph , voltage graph , mathematical optimization , mathematics , artificial intelligence , physics , classical mechanics
Network maximum flow problem is important and basic in graph theory, and one of its research directions is maximum-flow acceleration in large-scale graph. Existing acceleration strategy includes graph contraction and parallel computation, where there is still room for improvement:(1) The existing two acceleration strategies are not fully integrated, leading to their limited acceleration effect; (2) There is no sufficient support for computing multiple maximum-flow in one graph, leading to a lot of redundant computation. (3)The existing preprocessing methods need to consider node degrees and capacity constraints, resulting in high computational complexity. To address above problems, we identify the bi-connected components in a given graph and build an overlay, which can help split the maximum-flow problem into several subproblems and then solve them in parallel. The algorithm only uses the connectivity in the graph and has low complexity. The analyses and experiments on benchmark graphs indicate that the method can significantly shorten the calculation time in large sparse graphs.