Contraction network for solving maximum flow problem
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
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This article covers a problem that often arises in real life situations-the maximum flow problem. This problem is a classical combinatorial optimization problem, which arises in many engineering and scientific applications. It is also the important content of computer science and operational research. In order to reduce computational complexity, many improvement methods have been proposed. In this paper, applying the thought of granular computing and combining the maximal clique, a new method is proposed to solve the approximately maximum flow problem in complex and large-scale network. By contracting the appropriate maximal clique in the network, we can calculate approximate maximum flow much more efficiently. Experimental results demonstrate that the algorithm we proposed can be used to compute approximate maximum flow in a relatively short period of time and also provide a new approach to the study on the maximum flow problem.