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DOI: 10.4230/LIPIcs.ISAAC.2019.29

Local Cliques in ER-Perturbed Random Geometric Graphs

Authors: Matthew Kahle, Minghao Tian, and Yusu Wang

Published in: LIPIcs, Volume 149, 30th International Symposium on Algorithms and Computation (ISAAC 2019)

Abstract

We study a random graph model introduced in [Srinivasan Parthasarathy et al., 2017] where one adds Erdős - Rényi (ER) type perturbation to a random geometric graph. More precisely, assume G_X^* is a random geometric graph sampled from a nice measure on a metric space X = (X,d). An ER-perturbed random geometric graph G^(p,q) is generated by removing each existing edge from G_X^* with probability p, while inserting each non-existent edge to G_X^* with probability q. We consider a localized version of clique number for G^(p,q): Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge. We show that the edge clique number presents two fundamentally different types of behaviors in G^(p,q), depending on which "type" of randomness it is generated from. As an application of the above results, we show that by a simple filtering process based on the edge clique number, we can recover the shortest-path metric of the random geometric graph G_X^* within a multiplicative factor of 3 from an ER-perturbed observed graph G^(p,q), for a significantly wider range of insertion probability q than what is required in [Srinivasan Parthasarathy et al., 2017].

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Matthew Kahle, Minghao Tian, and Yusu Wang. Local Cliques in ER-Perturbed Random Geometric Graphs. In 30th International Symposium on Algorithms and Computation (ISAAC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 149, pp. 29:1-29:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{kahle_et_al:LIPIcs.ISAAC.2019.29,
  author =	{Kahle, Matthew and Tian, Minghao and Wang, Yusu},
  title =	{{Local Cliques in ER-Perturbed Random Geometric Graphs}},
  booktitle =	{30th International Symposium on Algorithms and Computation (ISAAC 2019)},
  pages =	{29:1--29:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-130-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{149},
  editor =	{Lu, Pinyan and Zhang, Guochuan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2019.29},
  URN =		{urn:nbn:de:0030-drops-115253},
  doi =		{10.4230/LIPIcs.ISAAC.2019.29},
  annote =	{Keywords: random graphs, random geometric graphs, edge clique number, the probabilistic method, metric recovery}
}

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DOI: 10.4230/LIPIcs.SoCG.2017.53

A Quest to Unravel the Metric Structure Behind Perturbed Networks

Authors: Srinivasan Parthasarathy, David Sivakoff, Minghao Tian, and Yusu Wang

Published in: LIPIcs, Volume 77, 33rd International Symposium on Computational Geometry (SoCG 2017)

Abstract

Graphs and network data are ubiquitous across a wide spectrum of scientific and application domains. Often in practice, an input graph can be considered as an observed snapshot of a (potentially continuous) hidden domain or process. Subsequent analysis, processing, and inferences are then performed on this observed graph. In this paper we advocate the perspective that an observed graph is often a noisy version of some discretized 1-skeleton of a hidden domain, and specifically we will consider the following natural network model: We assume that there is a true graph G^* which is a certain proximity graph for points sampled from a hidden domain X; while the observed graph G is an Erdos-Renyi type perturbed version of G^*. Our network model is related to, and slightly generalizes, the much-celebrated small-world network model originally proposed by Watts and Strogatz. However, the main question we aim to answer is orthogonal to the usual studies of network models (which often focuses on characterizing / predicting behaviors and properties of real-world networks). Specifically, we aim to recover the metric structure of G^* (which reflects that of the hidden space X as we will show) from the observed graph G. Our main result is that a simple filtering process based on the Jaccard index can recover this metric within a multiplicative factor of 2 under our network model. Our work makes one step towards the general question of inferring structure of a hidden space from its observed noisy graph representation. In addition, our results also provide a theoretical understanding for Jaccard-Index-based denoising approaches.

Cite as

Srinivasan Parthasarathy, David Sivakoff, Minghao Tian, and Yusu Wang. A Quest to Unravel the Metric Structure Behind Perturbed Networks. In 33rd International Symposium on Computational Geometry (SoCG 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 77, pp. 53:1-53:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{parthasarathy_et_al:LIPIcs.SoCG.2017.53,
  author =	{Parthasarathy, Srinivasan and Sivakoff, David and Tian, Minghao and Wang, Yusu},
  title =	{{A Quest to Unravel the Metric Structure Behind Perturbed Networks}},
  booktitle =	{33rd International Symposium on Computational Geometry (SoCG 2017)},
  pages =	{53:1--53:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-038-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{77},
  editor =	{Aronov, Boris and Katz, Matthew J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2017.53},
  URN =		{urn:nbn:de:0030-drops-72112},
  doi =		{10.4230/LIPIcs.SoCG.2017.53},
  annote =	{Keywords: metric structure, Erd\"{o}s-R\'{e}nyi perturbation, graphs, doubling measure}
}

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Local Cliques in ER-Perturbed Random Geometric Graphs

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A Quest to Unravel the Metric Structure Behind Perturbed Networks

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