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On The Performance Of Percolation Graph Matching

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Value graph_match_percolation returns an object of class „graphMatch“ which is a list containing the following components: corr_A matching correspondence in G 1 G_1 G1 corr_B matching correspondence in G 2 G_2 G2 match_order the order of vertices getting matched seeds a vector of logicals indicating if the corresponding vertex is a seed References L. Yartseva and M. 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 di cult to design systems and predict their behavior. Value graph_match_percolation returns an object of class “ graphMatch “ which is a list containing the following components: corr_A matching correspondence in G_1 corr_B matching correspondence in G_2 match_order the order of vertices getting matched seeds a vector of logicals indicating if the corresponding vertex is a seed References L. Yartseva and

An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. Among them, percolation-based graph matching (PGM) has been explored to identify entities belonging to a same user across two different networks based on a set of initial pre-matched seed nodes

Social Network deanonymization

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The most scalable graph{matching approaches use ideas from percolation graph theory, where a matched node pair \infects“ neighbouring node pairs as additional potential matches. This class of matching algorithms requires an ini-tial seed set of known matches to start the percolation. The size and correctness of the matching is very sensitive to the size of the seed set.

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 di cult to design systems and predict their behavior. Graph matching aims to find the latent vertex correspondence between two edge-correlated graphs and has found numerous applications across different fields. In this paper, we study a seeded graph matching problem, which assumes that a set of seeds, i.e.,

otic performance of graph match-ing algorithms applied to large systems. Specifically, when the social network is modeled as an Erd ̈os–R ́enyi random graph, it has been shown in [Pedarsani and Grossglauser 2011] that, under mild conditions, users participating in two different social networks can be successfully mat An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks.

We also confirm through experiments that the performance of percolation graph matching is surprisingly good, both for synthetic graphs and real social-network data. Driven by many real applications, we study the problem of seeded graph matching. Given two graphs and , and a small set of pre-matched node pairs where and , the problem is to identify a matching between and growing from , such that each pair in the An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks.

Seeded graph matching via large neighborhood statistics

We consider the graph matching/similarity problem of determining how similar two given graphs G0, G1 are and recovering the permutation n on the vertices of G1 that minimizes the symmetric difference between the edges of G0 and π (G1). Graph matching/similarity has applications for pattern matching, computer vision, social network anonymization, malware analysis, and more.

  • Percolation Graph Matching Methods — graph_match_percolation
  • On the Performance of Percolation Graph Matching
  • Seeded graph matching via large neighborhood statistics
  • "On the performance of percolation graph matching."

文章浏览阅读882次。本文探讨了社交网络中去匿名化的两种情境：seeded和seedless，并介绍了在seeded情境下，通过bootstrappercolation进行去匿名化的四种典型研究方法。包括percolation graph matching的性能评估、在幂律随机图上的bootstrappercolation、社交网络高效调和算法及社区增强的在线社交网络去匿名化。

An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. Abstract—Linking multiple accounts owned by the same user across different online social networks (OSNs) is an important issue in social networks, known as identity reconciliation. Graph matching is one of popular techniques to solve this problem by identifying a map that matches a set of vertices across different OSNs. Among them, percolation-based graph matching (PGM)

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Bibliographic details on On the performance of percolation graph matching.

We show that this algorithm can correct all errors in graph matching for stochastic block model graphs with high probability. Finally, we apply our algorithm as a post-processing step for other approximate graph matching algorithms to significantly improve the performance of state-of-the-art algorithms for seedless graph matching. In this paper, we give a new graph–matching algorithm that can operate with a much smaller seed set than previous approaches, with only a small increase in matching errors. We characterize a phase transition in matching performance as a function of the seed set size, using a random bigraph model and ideas from bootstrap percolation theory. We also show the

Publications by Matthias Grossglauser

An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. 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 di cult to design systems and predict their behavior.

In this paper, we introduce the attributed graph alignment problem, where additional user information, referred to as attributes, is incorporated to assist graph alignment. We establish both the achievability and converse results on recovering vertex correspondence exactly, where the conditions match for certain parameter regimes. 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 di cult to design systems and predict their behavior. Fig. 1. Total number of matched nodes vs number of seeds, for different graphs and algorithms, in the case of s = 0.7 – „De-anonymizing scale-free social networks by percolation graph matching“

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.

An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks.

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 di cult to design systems and predict their behavior. In this paper, we give a new graph{matching algorithm that can operate with a much smaller seed set than pre-vious approaches, with only a small increase in matching errors. We characterize a phase transition in matching per-formance as a function of the seed set size, using a random bigraph model and ideas from bootstrap percolation theory. We also show the excellent

An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. Key words and phrases. Percolation, site percolation, critical probability, hyperbolic plane, matching graph. 1 Figure 1.1. The square lattice Z2 and its matching graph. account of site percolation on the triangular lattice, and a discussion of site percola-tion on a so-called ‘matching pair’ of planar lattices.

R: Percolation Graph Matching Methods