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Scalable approximations to k-cycle transversal problems on dynamic networks

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Abstract

We study scalable approximation algorithms for the k-cycle transversal problem, which is to find a minimum-size set of edges that intersects all simple cycles of length k in a network. This problem is relevant to network reliability through the important metric of network clustering coefficient of order k. We formulate two algorithms to be both scalable and have good solution quality in practice: CARL and DARC. DARC is able to efficiently update its solution under dynamic node and edge insertion and removal to the network. In our experimental evaluation, we demonstrate that DARC is able to run on networks with billions of 3-cycles within 2 h and is able to dynamically update its solution in microseconds.

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Notes

  1. The conference version of this paper studied the 3-cycle (triangle) version of this problem [15].

  2. https://gitlab.com/kuhnle/cycle-transversal.

  3. https://gitlab.com/kuhnle/cycle-transversal.

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Acknowledgements

This work was supported in part by NSFCCF-1422116, DTRAHDTRA1-14-1-0055 and NSF EFRI 1441231.

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Authors and Affiliations

  1. Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32603, USA

    Alan Kuhnle, Victoria G. Crawford & My T. Thai

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  1. Alan Kuhnle
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  2. Victoria G. Crawford
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  3. My T. Thai
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Correspondence to Alan Kuhnle.

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Kuhnle, A., Crawford, V.G. & Thai, M.T. Scalable approximations to k-cycle transversal problems on dynamic networks. Knowl Inf Syst 61, 65–84 (2019). https://doi.org/10.1007/s10115-018-1296-5

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