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Documents authored by Nguyen, Hoai-An

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Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2026.140
Streaming Complexity Separations for Dense and Sparse Graphs

Authors: Yang P. Liu, Hoai-An Nguyen, Noah G. Singer, and David P. Woodruff

Published in: LIPIcs, Volume 374, 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)

Abstract
We identify a sharp separation in the streaming space complexity of Maximum Cut when the algorithm must output an approximate cut (rather than only the approximate value). For dense graphs, we show that O(n/ε²) space is sufficient and that Ω(n) space is necessary. In contrast, for graphs with Θ(n/ε²) edges, the situation is markedly different: we show that the problem requires Ω(n log(ε² n)/ε²) space for any ε = ω(1/√n), which is tight for the full range of ε. We also give an Ω(n log n/ε²)-space lower bound against deterministic algorithms for outputting a (1-ε) approximation to the value of the maximum cut. Using similar techniques we prove an analogous sharp separation in the streaming space complexity of Densest Subgraph and show that for every constant-arity CSP over a constant-size alphabet and the Similarity problem the space complexity in dense streams can be improved by shaving a logarithmic factor.
Cite as

Yang P. Liu, Hoai-An Nguyen, Noah G. Singer, and David P. Woodruff. Streaming Complexity Separations for Dense and Sparse Graphs. In 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 374, pp. 140:1-140:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{liu_et_al:LIPIcs.ICALP.2026.140,
  author =	{Liu, Yang P. and Nguyen, Hoai-An and Singer, Noah G. and Woodruff, David P.},
  title =	{{Streaming Complexity Separations for Dense and Sparse Graphs}},
  booktitle =	{53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)},
  pages =	{140:1--140:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-428-4},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{374},
  editor =	{Bhattacharya, Sayan and Nanongkai, Danupon and Benedikt, Michael and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2026.140},
  URN =		{urn:nbn:de:0030-drops-265290},
  doi =		{10.4230/LIPIcs.ICALP.2026.140},
  annote =	{Keywords: streaming, maximum cut, complexity separations}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2022.48
Asymptotically Optimal Bounds for Estimating H-Index in Sublinear Time with Applications to Subgraph Counting

Authors: Sepehr Assadi and Hoai-An Nguyen

Published in: LIPIcs, Volume 245, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)

Abstract
The h-index is a metric used to measure the impact of a user in a publication setting, such as a member of a social network with many highly liked posts or a researcher in an academic domain with many highly cited publications. Specifically, the h-index of a user is the largest integer h such that at least h publications of the user have at least h units of positive feedback. We design an algorithm that, given query access to the n publications of a user and each publication’s corresponding positive feedback number, outputs a (1± ε)-approximation of the h-index of this user with probability at least 1-δ in time O(n⋅ln(1/δ) / (ε²⋅h)), where h is the actual h-index which is unknown to the algorithm a-priori. We then design a novel lower bound technique that allows us to prove that this bound is in fact asymptotically optimal for this problem in all parameters n,h,ε, and δ. Our work is one of the first in sublinear time algorithms that addresses obtaining asymptotically optimal bounds, especially in terms of the error and confidence parameters. As such, we focus on designing novel techniques for this task. In particular, our lower bound technique seems quite general - to showcase this, we also use our approach to prove an asymptotically optimal lower bound for the problem of estimating the number of triangles in a graph in sublinear time, which now is also optimal in the error and confidence parameters. This latter result improves upon prior lower bounds of Eden, Levi, Ron, and Seshadhri (FOCS'15) for this problem, as well as multiple follow-up works that extended this lower bound to other subgraph counting problems.
Cite as

Sepehr Assadi and Hoai-An Nguyen. Asymptotically Optimal Bounds for Estimating H-Index in Sublinear Time with Applications to Subgraph Counting. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 48:1-48:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{assadi_et_al:LIPIcs.APPROX/RANDOM.2022.48,
  author =	{Assadi, Sepehr and Nguyen, Hoai-An},
  title =	{{Asymptotically Optimal Bounds for Estimating H-Index in Sublinear Time with Applications to Subgraph Counting}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)},
  pages =	{48:1--48:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-249-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{245},
  editor =	{Chakrabarti, Amit and Swamy, Chaitanya},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2022.48},
  URN =		{urn:nbn:de:0030-drops-171708},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2022.48},
  annote =	{Keywords: Sublinear time algorithms, h-index, asymptotically optimal bounds, lower bounds, subgraph counting}
}
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