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ChaoWei Ou, Sanjay Ranka, "Parallel Incremental Graph Partitioning," IEEE Transactions on Parallel and Distributed Systems, vol. 8, no. 8, pp. 884896, August, 1997.  
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@article{ 10.1109/71.605773, author = {ChaoWei Ou and Sanjay Ranka}, title = {Parallel Incremental Graph Partitioning}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {8}, number = {8}, issn = {10459219}, year = {1997}, pages = {884896}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.605773}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  Parallel Incremental Graph Partitioning IS  8 SN  10459219 SP884 EP896 EPD  884896 A1  ChaoWei Ou, A1  Sanjay Ranka, PY  1997 KW  Linearprogramming KW  mapping KW  parallel KW  refinement KW  remapping. VL  8 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—Partitioning graphs into equally large groups of nodes while minimizing the number of edges between different groups is an extremely important problem in parallel computing. For instance, efficiently parallelizing several scientific and engineering applications requires the partitioning of data or tasks among processors such that the computational load on each node is roughly the same, while communication is minimized. Obtaining exact solutions is computationally intractable, since graph partitioning is an NPcomplete.
For a large class of irregular and adaptive data parallel applications (such as adaptive graphs), the computational structure changes from one phase to another in an incremental fashion. In incremental graphpartitioning problems the partitioning of the graph needs to be updated as the graph changes over time; a small number of nodes or edges may be added or deleted at any given instant.
In this paper, we use a linear programmingbased method to solve the incremental graphpartitioning problem. All the steps used by our method are inherently parallel and hence our approach can be easily parallelized. By using an initial solution for the graph partitions derived from recursive spectral bisectionbased methods, our methods can achieve repartitioning at considerably lower cost than can be obtained by applying recursive spectral bisection. Further, the quality of the partitioning achieved is comparable to that achieved by applying recursive spectral bisection to the incremental graphs from scratch.
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