Abstract
We consider the problem introduced by Korach and Stern (Mathematical Programming, 98:345–414, 2003) of building a network given connectivity constraints. A network designer is given a set of vertices \(V\) and constraints \(S_i \subseteq V\), and seeks to build the lowest cost set of edges \(E\) such that each \(S_i\) induces a connected subgraph of \((V,E)\). First, we answer a question posed by Korach and Stern (Discrete Applied Mathematics, 156:444–450, 2008): for the offline version of the problem, we prove an \(\varOmega (\log n)\) hardness of approximation result for uniform cost networks (where edge costs are all \(1\)) and give an algorithm that almost matches this bound, even in the arbitrary cost case. Then we consider the online problem, where the constraints must be satisfied as they arrive. We give an \(O(n\log n)\)-competitive algorithm for the arbitrary cost online problem, which has an \(\varOmega (n)\)-competitive lower bound. We look at the uniform cost case as well and give an \(O(n^{2/3}\log ^{2/3} n)\)-competitive algorithm against an oblivious adversary, as well as an \(\varOmega (\sqrt{n})\)-competitive lower bound against an adaptive adversary. We also examine cases when the underlying network graph is known to be a star or a path and prove matching upper and lower bounds of \(\Theta (\log n)\) on the competitive ratio for them.
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Notes
For more on CDC statistics, we direct the reader to www.cdc.gov/datastatistics/.
Alon et al. (2006) argue that we can use all edges of cost less than \(\alpha /m\) and stay within our bound, and we can ignore all edges with cost greater than \(\alpha \), and then rescale. They also show how to guess \(\alpha \) to within a factor of \(2\), justifying the assumption that \(\alpha \) is known in advance.
References
Alon N, Asodi V (2005) Learning a hidden subgraph. SIAM J Discret Math 18(4):697–712
Alon N, Awerbuch B, Azar Y, Buchbinder N, Naor J (2003) The online set cover problem. In: Proceedings of the 35th annual ACM symposium on theory of, computing, pp. 100–105
Alon N, Awerbuch B, Azar Y, Buchbinder N, Naor J (2006) A general approach to online network optimization problems. ACM Trans Algorithms 2(4):640–660
Alon N, Beigel R, Kasif S, Rudich S, Sudakov B (2004) Learning a hidden matching. SIAM J Comput 33(2):487–501
Angluin D, Aspne, J, Reyzin L (2008) Optimally learning social networks with activations and suppressions. In: 19th International conference on algorithmic learning theory, pp. 272–286
Angluin D, Aspnes J, Reyzin L (2010) Inferring social networks from outbreaks. In: 21st International conference on algorithmic learning theory, pp. 104–118
Angluin D, Chen J (2008) Learning a hidden graph using \(O(\log n)\) queries per edge. J Comput Syst Sci 74(4):546–556
Beigel R, Alon N, Kasif S, Apaydin MS, Fortnow L (2001) An optimal procedure for gap closing in whole genome shotgun sequencing. In: RECOMB, pp. 22–30
Booth KS, Lueker GS (1976) Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms. J Comput Syst Sci 13(3):335–379
Buchbinder N (2008) Designing competitive online algorithms via a primal-dual approach. Ph.D. thesis, Technion–Israel Institute of Technology, Haifa, Israel
Chockler G, Melamed R, Tock Y, Vitenberg R (2007) Constructing scalable overlays for pub-sub with many topics. In: PODC, pp. 109–118
Erdös P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61
Feige U (1998) A threshold of ln for approximating set cover. J ACM 45(4):634–652
Gomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. TKDD 5(4):21
Grebinski V, Kucherov G (1998) Reconstructing a hamiltonian cycle by querying the graph: application to DNA physical mapping. Discret Appl Math 88(1–3):147–165
Gupta A, Krishnaswamy R, Ravi R (2009) Online and stochastic survivable network design. In: 41st Symposium on the theory of, computing, pp. 685–694
Korach E, Stern M (2003) The clustering matroid and the optimal clustering tree. Math Program 98 (1–3):345–414
Korach E, Stern M (2008) The complete optimal stars-clustering-tree problem. Discret Appl Math 156(4):444–450
Raz R, Safra S (1997) A sub-constant error-probability low-degree test, and a sub-constant error-probability pcp characterization of np. In: STOC, pp. 475–484
Reyzin L, Srivastava N (2007) Learning and verifying graphs using queries with a focus on edge counting. In: 18th International conference on algorithmic learning theory, pp. 285–297
Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: KES (3), pp. 67–75
Wolsey LA (1982) An analysis of the greedy algorithm for the submodular set covering problem. Combinatorica 2(4):385–393
Acknowledgments
We thank an anonymous reviewer of a previous version of this paper for helping improve our analysis of Theorem 2. Dana Angluin acknowledges this work was supported in part by the National Science Foundation under grant CCF-0916389. James Aspnes acknowledges this work was supported in part by the National Science Foundation under grants CNS-0435201 and CCF-0916389. Parts of this work were done while Lev Reyzin was in the Department of Computer Science at Yale University and in Yahoo! Research, NY. Lev Reyzin acknowledges that this work was supported in part by the National Science Foundation under a National Science Foundation Graduate Research Fellowship and in part under Grant #0937060 to the Computing Research Association for the Computing Innovation Fellowship program. This paper is based on an earlier conference version addressing the passive inference of social networks (Angluin et al. 2010).
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Appendix: updating a pq-tree
Appendix: updating a pq-tree
We briefly describe the patterns in Booth and Lueker (1976) for updating pq-trees, as broken down into \(10\) cases.
This can be used as a guide for tracking the changes in Eq. 2.
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L
This pattern simply relabels some leaf nodes.
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P1
This pattern simply relabels a p-node.
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P2
This pattern moves some children of a p-node into their own p-node.
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P3
This pattern moves some children of a p-node into their own p-node and creates a parent q-node.
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P4
This pattern moves some children of a p-node to be children of a newly created p-node, whose parent is a q-node that is a child of the original p-node.
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P5
This pattern moves some children of a p-node into their own p-node that is the child of the original p-node, which becomes transformed to a q-node.
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P6
This pattern moves some children of a p-node to their own p-node that is moved to be the child of a newly created q-node formed by merging two q-nodes.
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Q1
This pattern simply relabels a q-node.
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Q2
This pattern deletes a q-node and moves its children to become children of its parent q-node.
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Q3
This pattern deletes two q-nodes and merges their children to become children of their parent q-node.
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Angluin, D., Aspnes, J. & Reyzin, L. Network construction with subgraph connectivity constraints. J Comb Optim 29, 418–432 (2015). https://doi.org/10.1007/s10878-013-9603-2
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DOI: https://doi.org/10.1007/s10878-013-9603-2