A strong formulation for the graph partition problem

Networks ◽  
2019 ◽  
Vol 75 (2) ◽  
pp. 183-202
Author(s):  
Sunil Chopra ◽  
Sangho Shim
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Xianyue Li ◽  
Yufei Pang ◽  
Chenxia Zhao ◽  
Yang Liu ◽  
Qingzhen Dong

AbstractGraph partition is a classical combinatorial optimization and graph theory problem, and it has a lot of applications, such as scientific computing, VLSI design and clustering etc. In this paper, we study the partition problem on large scale directed graphs under a new objective function, a new instance of graph partition problem. We firstly propose the modeling of this problem, then design an algorithm based on multi-level strategy and recursive partition method, and finally do a lot of simulation experiments. The experimental results verify the stability of our algorithm and show that our algorithm has the same good performance as METIS. In addition, our algorithm is better than METIS on unbalanced ratio.


1990 ◽  
Vol 76 (2-3) ◽  
pp. 343-351
Author(s):  
R. Sarnath ◽  
Xin He

Networks ◽  
1996 ◽  
Vol 28 (4) ◽  
pp. 221-225 ◽  
Author(s):  
Dorit S. Hochbaum ◽  
Anu Pathria

2015 ◽  
Vol 122 (10) ◽  
pp. 972
Author(s):  
Sebastian M. Cioabă ◽  
Peter J. Cameron

Networks ◽  
1999 ◽  
Vol 33 (3) ◽  
pp. 189-191 ◽  
Author(s):  
Bettina Klinz ◽  
Gerhard J. Woeginger

2004 ◽  
Vol 14 (01n02) ◽  
pp. 85-104 ◽  
Author(s):  
XIAODONG WU ◽  
DANNY Z. CHEN ◽  
JAMES J. MASON ◽  
STEVEN R. SCHMID

Data clustering is an important theoretical topic and a sharp tool for various applications. It is a task frequently arising in geometric computing. The main objective of data clustering is to partition a given data set into clusters such that the data items within the same cluster are "more" similar to each other with respect to certain measures. In this paper, we study the pairwise data clustering problem with pairwise similarity/dissimilarity measures that need not satisfy the triangle inequality. By using a criterion, called the minimum normalized cut, we model the general pairwise data clustering problem as a graph partition problem. The graph partition problem based on minimizing the normalized cut is known to be NP-hard. For an undirected weighted graph of n vertices, we present a ((4+o(1)) In n)-approximation polynomial time algorithm for the minimum normalized cut problem; this is the first provably good approximation polynomial time algorithm for the problem. We also give a more efficient algorithm for this problem by sacrificing the approximation ratio slightly. Further, our scheme achieves a ((2+o(1)) In n)-approximation polynomial time algorithm for computing the sparsest cuts in edge-weighted and vertex-weighted undirected graphs, improving the previously best known approximation ratio by a constant factor. Some applications and implementation work of our approximation normalized cut algorithms are also discussed.


1996 ◽  
Vol 12 (4) ◽  
pp. 393-400 ◽  
Author(s):  
Yanpei Liu ◽  
Morgana Aurora ◽  
Simeone Bruno

1992 ◽  
Vol 43 (2) ◽  
pp. 87-94 ◽  
Author(s):  
Arunabha Sen ◽  
Haiyong Deng ◽  
Sumanta Guha

2021 ◽  
Author(s):  
Xianyue Li ◽  
Yufei Pang ◽  
Chenxia Zhao ◽  
Yang Liu ◽  
Qingzhen Dong

Abstract Graph partition is a classical combinatorial optimization and graph theory problem, and it has a lot of applications, such as scientific computing, VLSI design and clustering etc. In this paper, we study the partition problem on large scale directed graphs under a new objective function, a new case of graph partition problem. We firstly propose the modeling of this problem, then design an algorithm based on multi-level strategy and recursive partition method, and finally do a lot of simulation experiments. The experimental results verify the stability of our algorithm and show that our algorithm has the same good performance as METIS. In addition, our algorithm is better than METIS on unbalanced ratio.


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