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On the performance of percolation graph matching

Published: 07 October 2013 Publication History

Abstract

Graph matching is a generalization of the classic graph isomorphism problem. By using only their structures a graph-matching algorithm finds a map between the vertex sets of two similar graphs. This has applications in the de-anonymization of social and information networks and, more generally, in the merging of structural data from different domains.
One class of graph-matching algorithms starts with a known seed set of matched node pairs. Despite the success of these algorithms in practical applications, their performance has been observed to be very sensitive to the size of the seed set. The lack of a rigorous understanding of parameters and performance makes it difficult to design systems and predict their behavior.
In this paper, we propose and analyze a very simple percolation - based graph matching algorithm that incrementally maps every pair of nodes (i,j) with at least r neighboring mapped pairs. The simplicity of this algorithm makes possible a rigorous analysis that relies on recent advances in bootstrap percolation theory for the G(n,p) random graph. We prove conditions on the model parameters in which percolation graph matching succeeds, and we establish a phase transition in the size of the seed set. We also confirm through experiments that the performance of percolation graph matching is surprisingly good, both for synthetic graphs and real social-network data.

Supplementary Material

PDF File (p119-yartseva-corrected.pdf)
The original version of the paper had a mistake in the proof of Lemma 2 in the appendix, which we correct in the revised version. The corrected proof has two parts. In the first part, we show that early in the matching process, no wrong pair $[i,j]$ receives $r$ marks or more, i.e., no wrong pairs are matched (w.h.p.) In the second part, we show that late in the matching process, if a wrong pair $[i,j]$ reaches $r$ marks, then either $[i,i]$ or $[j,j]$ lready received $r$ marks before (w.h.p.), and as a consequence $[i,j]$ does not get matched. Together, this establishes a sufficient condition for no matching error to occur. However, as a consequence of this correction, the condition on the parameter $r$ in Lemma 2 (threshold for number of marks until a node pair gets matched) is now $r >= 4$, as opposed to $r>=2$. This is reflected in the discussion of the result below Lemma 2. The main findings and conclusions of the paper are otherwise unchanged.

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Cited By

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  • (2024)Gotta Match 'Em All: Solution Diversification in Graph Matching Matched FiltersIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2024.346792110(752-764)Online publication date: 2024
  • (2024)Attributed Graph AlignmentIEEE Transactions on Information Theory10.1109/TIT.2024.340381070:8(5910-5934)Online publication date: Aug-2024
  • (2024)Database Matching Under Noisy Synchronization ErrorsIEEE Transactions on Information Theory10.1109/TIT.2024.338899070:6(4335-4367)Online publication date: Jun-2024
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    cover image ACM Conferences
    COSN '13: Proceedings of the first ACM conference on Online social networks
    October 2013
    254 pages
    ISBN:9781450320849
    DOI:10.1145/2512938
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 07 October 2013

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    Author Tags

    1. bootstrap percolation
    2. de-anonymization
    3. graph matching
    4. graph sampling
    5. social networks

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    COSN'13: Conference on Online Social Networks
    October 7 - 8, 2013
    Massachusetts, Boston, USA

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    COSN '13 Paper Acceptance Rate 22 of 138 submissions, 16%;
    Overall Acceptance Rate 69 of 307 submissions, 22%

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    View all
    • (2024)Gotta Match 'Em All: Solution Diversification in Graph Matching Matched FiltersIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2024.346792110(752-764)Online publication date: 2024
    • (2024)Attributed Graph AlignmentIEEE Transactions on Information Theory10.1109/TIT.2024.340381070:8(5910-5934)Online publication date: Aug-2024
    • (2024)Database Matching Under Noisy Synchronization ErrorsIEEE Transactions on Information Theory10.1109/TIT.2024.338899070:6(4335-4367)Online publication date: Jun-2024
    • (2024)On the Feasible Region of Efficient Algorithms for Attributed Graph AlignmentIEEE Transactions on Information Theory10.1109/TIT.2024.335110770:5(3622-3639)Online publication date: 8-Jan-2024
    • (2024)Exact Graph Matching in Correlated Gaussian-Attributed Erdős- Rényi Mode2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619555(3450-3455)Online publication date: 7-Jul-2024
    • (2024)A Polynomial Time Iterative Algorithm for Matching Gaussian Matrices with Non-vanishing CorrelationFoundations of Computational Mathematics10.1007/s10208-024-09662-xOnline publication date: 22-Jul-2024
    • (2024)Advances in Privacy Preservation TechnologiesPrivacy Computing10.1007/978-981-99-4943-4_2(17-42)Online publication date: 13-Feb-2024
    • (2024)A polynomial‐time approximation scheme for the maximal overlap of two independent Erdős–Rényi graphsRandom Structures & Algorithms10.1002/rsa.2121265:1(220-257)Online publication date: 7-Mar-2024
    • (2023)SeedGNNProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620099(40390-40411)Online publication date: 23-Jul-2023
    • (2023)Efficient algorithms for exact graph matching on correlated stochastic block models with constant correlationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620053(39416-39452)Online publication date: 23-Jul-2023
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