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Documents authored by Amanatidis, Georgios

Document
DOI: 10.4230/LIPIcs.STACS.2023.7
Approximate Sampling and Counting of Graphs with Near-Regular Degree Intervals

Authors: Georgios Amanatidis and Pieter Kleer

Published in: LIPIcs, Volume 254, 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023)

Abstract
The approximate uniform sampling of graphs with a given degree sequence is a well-known, extensively studied problem in theoretical computer science and has significant applications, e.g., in the analysis of social networks. In this work we study a generalization of the problem, where degree intervals are specified instead of a single degree sequence. We are interested in sampling and counting graphs whose degree sequences satisfy the corresponding degree interval constraints. A natural scenario where this problem arises is in hypothesis testing on networks that are only partially observed. We provide the first fully polynomial almost uniform sampler (FPAUS) as well as the first fully polynomial randomized approximation scheme (FPRAS) for sampling and counting, respectively, graphs with near-regular degree intervals, i.e., graphs in which every node has a degree from an interval not too far away from a given r ∈ ℕ. In order to design our FPAUS, we rely on various state-of-the-art tools from Markov chain theory and combinatorics. In particular, by carefully using Markov chain decomposition and comparison arguments, we reduce part of our problem to the recent breakthrough of Anari, Liu, Oveis Gharan, and Vinzant (2019) on sampling a base of a matroid under a strongly log-concave probability distribution, and we provide the first non-trivial algorithmic application of a breakthrough asymptotic enumeration formula of Liebenau and Wormald (2017). As a more direct approach, we also study a natural Markov chain recently introduced by Rechner, Strowick and Müller-Hannemann (2018), based on three local operations - switches, hinge flips, and additions/deletions of an edge. We obtain the first theoretical results for this Markov chain, showing it is rapidly mixing for the case of near-regular degree intervals of size at most one.
Cite as

Georgios Amanatidis and Pieter Kleer. Approximate Sampling and Counting of Graphs with Near-Regular Degree Intervals. In 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 254, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{amanatidis_et_al:LIPIcs.STACS.2023.7,
  author =	{Amanatidis, Georgios and Kleer, Pieter},
  title =	{{Approximate Sampling and Counting of Graphs with Near-Regular Degree Intervals}},
  booktitle =	{40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023)},
  pages =	{7:1--7:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-266-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{254},
  editor =	{Berenbrink, Petra and Bouyer, Patricia and Dawar, Anuj and Kant\'{e}, Mamadou Moustapha},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2023.7},
  URN =		{urn:nbn:de:0030-drops-176596},
  doi =		{10.4230/LIPIcs.STACS.2023.7},
  annote =	{Keywords: graph sampling, degree interval, degree sequence, Markov Chain Monte Carlo method, switch Markov chain}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2016.9
Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results

Authors: Georgios Amanatidis, Evangelos Markakis, and Krzysztof Sornat

Published in: LIPIcs, Volume 58, 41st International Symposium on Mathematical Foundations of Computer Science (MFCS 2016)

Abstract
We study a revenue maximization problem in the context of social networks. Namely, we consider a model introduced by Alon, Mansour, and Tennenholtz (EC 2013) that captures inequity aversion, i.e., prices offered to neighboring vertices should not be significantly different. We first provide approximation algorithms for a natural class of instances, referred to as the class of single-value revenue functions. Our results improve on the current state of the art, especially when the number of distinct prices is small. This applies, for example, to settings where the seller will only consider a fixed number of discount types or special offers. We then resolve one of the open questions posed in Alon et al., by establishing APX-hardness for the problem. Surprisingly, we further show that the problem is NP-complete even when the price differences are allowed to be relatively large. Finally, we also provide some extensions of the model of Alon et al., regarding the allowed set of prices.
Cite as

Georgios Amanatidis, Evangelos Markakis, and Krzysztof Sornat. Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results. In 41st International Symposium on Mathematical Foundations of Computer Science (MFCS 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 58, pp. 9:1-9:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{amanatidis_et_al:LIPIcs.MFCS.2016.9,
  author =	{Amanatidis, Georgios and Markakis, Evangelos and Sornat, Krzysztof},
  title =	{{Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results}},
  booktitle =	{41st International Symposium on Mathematical Foundations of Computer Science (MFCS 2016)},
  pages =	{9:1--9:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-016-3},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{58},
  editor =	{Faliszewski, Piotr and Muscholl, Anca and Niedermeier, Rolf},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2016.9},
  URN =		{urn:nbn:de:0030-drops-64254},
  doi =		{10.4230/LIPIcs.MFCS.2016.9},
  annote =	{Keywords: inequity aversion, social networks, revenue maximization}
}
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