Unraveling Universally Closest Refinements via Symmetric Density Decomposition and Fisher Market Equilibrium

Authors T-H. Hubert Chan , Quan Xue



PDF
Thumbnail PDF

File

LIPIcs.ITCS.2025.35.pdf
  • Filesize: 0.85 MB
  • 23 pages

Document Identifiers

Author Details

T-H. Hubert Chan
  • Department of Computer Science, University of Hong Kong, Hong Kong
Quan Xue
  • Department of Computer Science, University of Hong Kong, Hong Kong

Cite As Get BibTex

T-H. Hubert Chan and Quan Xue. Unraveling Universally Closest Refinements via Symmetric Density Decomposition and Fisher Market Equilibrium. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 35:1-35:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ITCS.2025.35

Abstract

We investigate the closest distribution refinements problem, which involves a vertex-weighted bipartite graph as input, where the vertex weights on each side sum to 1 and represent a probability distribution. A refinement of one side’s distribution is an edge distribution that corresponds to distributing the weight of each vertex from that side to its incident edges. The objective is to identify a pair of distribution refinements for both sides of the bipartite graph such that the two edge distributions are as close as possible with respect to a specific divergence notion. This problem is a generalization of transportation, in which the special case occurs when the two closest distributions are identical. The problem has recently emerged in the context of composing differentially oblivious mechanisms.
Our main result demonstrates that a universal refinement pair exists, which is simultaneously closest under all divergence notions that satisfy the data processing inequality. Since differential obliviousness can be examined using various divergence notions, such a universally closest refinement pair offers a powerful tool in relation to such applications.
We discover that this pair can be achieved via locally verifiable optimality conditions. Specifically, we observe that it is equivalent to the following problems, which have been traditionally studied in distinct research communities: (1) hypergraph density decomposition, and (2) symmetric Fisher Market equilibrium.
We adopt a symmetric perspective of hypergraph density decomposition, in which hyperedges and nodes play equivalent roles. This symmetric decomposition serves as a tool for deriving precise characterizations of optimal solutions for other problems and enables the application of algorithms from one problem to another. This connection allows existing algorithms for computing or approximating the Fisher market equilibrium to be adapted for all the aforementioned problems. For example, this approach allows the well-known iterative proportional response process to provide approximations for the corresponding problems with multiplicative error in distributed settings, whereas previously, only absolute error had been achieved in these contexts. Our study contributes to the understanding of various problems within a unified framework, which may serve as a foundation for connecting other problems in the future.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Distribution functions
  • Theory of computation → Mathematical optimization
Keywords
  • closest distribution refinements
  • density decomposition
  • Fisher market equilibrium

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Albert Angel, Nikos Sarkas, Nick Koudas, and Divesh Srivastava. Dense subgraph maintenance under streaming edge weight updates for real-time story identification. Proceedings of the VLDB Endowment, 5(6):574-585, 2012. URL: https://doi.org/10.14778/2168651.2168658.
  2. Kenneth J Arrow and Gerard Debreu. Existence of an equilibrium for a competitive economy. Econometrica: Journal of the Econometric Society, pages 265-290, 1954. Google Scholar
  3. Oana Denisa Balalau, Francesco Bonchi, T.-H. Hubert Chan, Francesco Gullo, and Mauro Sozio. Finding subgraphs with maximum total density and limited overlap. In WSDM, pages 379-388. ACM, 2015. URL: https://doi.org/10.1145/2684822.2685298.
  4. Nikhil Bansal and Ilan Reuven Cohen. Contention resolution, matrix scaling and fair allocation. In WAOA, volume 12982 of Lecture Notes in Computer Science, pages 252-274. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-92702-8_16.
  5. Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences, 2(1):183-202, 2009. URL: https://doi.org/10.1137/080716542.
  6. Roger E Behrend. Fractional perfect b-matching polytopes i: General theory. Linear Algebra and its Applications, 439(12):3822-3858, 2013. Google Scholar
  7. Aaron Bernstein, Jacob Holm, and Eva Rotenberg. Online bipartite matching with amortized O(log ^2 n) replacements. J. ACM, 66(5):37:1-37:23, 2019. URL: https://doi.org/10.1145/3344999.
  8. Benjamin Birnbaum, Nikhil R Devanur, and Lin Xiao. Distributed algorithms via gradient descent for fisher markets. In Proceedings of the 12th ACM conference on Electronic commerce, pages 127-136, 2011. URL: https://doi.org/10.1145/1993574.1993594.
  9. Richard A Brualdi. Combinatorial matrix classes, volume 13. Cambridge University Press, 2006. Google Scholar
  10. Niv Buchbinder, Anupam Gupta, Daniel Hathcock, Anna R. Karlin, and Sherry Sarkar. Maintaining matroid intersections online. In SODA, pages 4283-4304. SIAM, 2024. URL: https://doi.org/10.1137/1.9781611977912.149.
  11. T.-H. Hubert Chan, Anand Louis, Zhihao Gavin Tang, and Chenzi Zhang. Spectral properties of hypergraph laplacian and approximation algorithms. J. ACM, 65(3):15:1-15:48, 2018. URL: https://doi.org/10.1145/3178123.
  12. T.-H. Hubert Chan and Quan Xue. Symmetric splendor: Unraveling universally closest refinements and fisher market equilibrium through density-friendly decomposition. CoRR, abs/2406.17964, 2024. URL: https://doi.org/10.48550/arXiv.2406.17964.
  13. Moses Charikar. Greedy approximation algorithms for finding dense components in a graph. In Klaus Jansen and Samir Khuller, editors, Approximation Algorithms for Combinatorial Optimization, pages 84-95, Berlin, Heidelberg, 2000. Springer Berlin Heidelberg. URL: https://doi.org/10.1007/3-540-44436-X_10.
  14. Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, and Sushant Sachdeva. Maximum flow and minimum-cost flow in almost-linear time. In FOCS, pages 612-623. IEEE, 2022. URL: https://doi.org/10.1109/FOCS54457.2022.00064.
  15. Maximilien Danisch, T.-H. Hubert Chan, and Mauro Sozio. Large scale density-friendly graph decomposition via convex programming. In WWW, pages 233-242. ACM, 2017. URL: https://doi.org/10.1145/3038912.3052619.
  16. Jinshuo Dong, Aaron Roth, and Weijie J Su. Gaussian differential privacy. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(1):3-37, 2022. Google Scholar
  17. Cynthia Dwork. Differential privacy. In ICALP (2), volume 4052 of Lecture Notes in Computer Science, pages 1-12. Springer, 2006. URL: https://doi.org/10.1007/11787006_1.
  18. Irving Fisher. Mathematical investigations in the theory of value and prices, 1892. Google Scholar
  19. Satoru Fujishige. Lexicographically optimal base of a polymatroid with respect to a weight vector. Mathematics of Operations Research, 5(2):186-196, 1980. URL: https://doi.org/10.1287/MOOR.5.2.186.
  20. Ashish Goel, Michael Kapralov, and Sanjeev Khanna. On the communication and streaming complexity of maximum bipartite matching. In SODA, pages 468-485. SIAM, 2012. URL: https://doi.org/10.1137/1.9781611973099.41.
  21. Andrew V Goldberg. Finding a maximum density subgraph, 1984. Google Scholar
  22. Elfarouk Harb, Kent Quanrud, and Chandra Chekuri. Faster and scalable algorithms for densest subgraph and decomposition. Advances in Neural Information Processing Systems, 35:26966-26979, 2022. Google Scholar
  23. Elfarouk Harb, Kent Quanrud, and Chandra Chekuri. Convergence to lexicographically optimal base in a (contra)polymatroid and applications to densest subgraph and tree packing. In ESA, volume 274 of LIPIcs, pages 56:1-56:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. URL: https://doi.org/10.4230/LIPICS.ESA.2023.56.
  24. Kamal Jain and Vijay V. Vazirani. Eisenberg-gale markets: Algorithms and game-theoretic properties. Games Econ. Behav., 70(1):84-106, 2010. URL: https://doi.org/10.1016/J.GEB.2008.11.011.
  25. Joseph B Kadane. Discrete search and the neyman-pearson lemma. Journal of Mathematical Analysis and Applications, 22(1):156-171, 1968. Google Scholar
  26. Samir Khuller and Barna Saha. On finding dense subgraphs. In International colloquium on automata, languages, and programming, pages 597-608. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-02927-1_50.
  27. Bernhard H. Korte. Combinatorial optimization: theory and algorithms. Springer, 2000. Google Scholar
  28. Euiwoong Lee and Sahil Singla. Maximum matching in the online batch-arrival model. ACM Trans. Algorithms, 16(4):49:1-49:31, 2020. URL: https://doi.org/10.1145/3399676.
  29. Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, and Xueqi Cheng. Flowscope: Spotting money laundering based on graphs. In Proceedings of the AAAI conference on artificial intelligence, volume 34(04), pages 4731-4738, 2020. URL: https://doi.org/10.1609/AAAI.V34I04.5906.
  30. Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks VS Lakshmanan, Wenjie Zhang, and Xuemin Lin. Efficient algorithms for densest subgraph discovery on large directed graphs. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 1051-1066, 2020. URL: https://doi.org/10.1145/3318464.3389697.
  31. Ilya Mironov. Rényi differential privacy. In CSF, pages 263-275. IEEE Computer Society, 2017. URL: https://doi.org/10.1109/CSF.2017.11.
  32. Rajeev Motwani, Rina Panigrahy, and Ying Xu. Fractional matching via balls-and-bins. In APPROX-RANDOM, volume 4110 of Lecture Notes in Computer Science, pages 487-498. Springer, 2006. URL: https://doi.org/10.1007/11830924_44.
  33. Yurii Nesterov. A method for solving the convex programming problem with convergence rate o(1/k²). Proceedings of the USSR Academy of Sciences, 269:543-547, 1983. URL: https://api.semanticscholar.org/CorpusID:145918791.
  34. Alexander Schrijver et al. Combinatorial optimization: polyhedra and efficiency, volume 24(2). Springer, 2003. Google Scholar
  35. Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos. Corescope: Graph mining using k-core analysis - Patterns, anomalies and algorithms. In 2016 IEEE 16th international conference on data mining (ICDM), pages 469-478. IEEE, 2016. URL: https://doi.org/10.1109/ICDM.2016.0058.
  36. Nikolaj Tatti. Density-friendly graph decomposition. ACM Transactions on Knowledge Discovery from Data (TKDD), 13(5):1-29, 2019. URL: https://doi.org/10.1145/3344210.
  37. Nikolaj Tatti and Aristides Gionis. Density-friendly graph decomposition. In Proceedings of the 24th International Conference on World Wide Web, pages 1089-1099, 2015. URL: https://doi.org/10.1145/2736277.2741119.
  38. Salil P. Vadhan and Wanrong Zhang. Concurrent composition theorems for differential privacy. In STOC, pages 507-519. ACM, 2023. URL: https://doi.org/10.1145/3564246.3585241.
  39. Fang Wu and Li Zhang. Proportional response dynamics leads to market equilibrium. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 354-363, 2007. URL: https://doi.org/10.1145/1250790.1250844.
  40. Li Zhang. Proportional response dynamics in the fisher market. In ICALP (2), volume 5556 of Lecture Notes in Computer Science, pages 583-594. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-02930-1_48.
  41. Mingxun Zhou, Elaine Shi, T.-H. Hubert Chan, and Shir Maimon. A theory of composition for differential obliviousness. In EUROCRYPT (3), volume 14006 of Lecture Notes in Computer Science, pages 3-34. Springer, 2023. URL: https://doi.org/10.1007/978-3-031-30620-4_1.
  42. Mingxun Zhou, Mengshi Zhao, T.-H. Hubert Chan, and Elaine Shi. Advanced composition theorems for differential obliviousness. In ITCS, volume 287 of LIPIcs, pages 103:1-103:24. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024. URL: https://doi.org/10.4230/LIPICS.ITCS.2024.103.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail