Improving the output of algorithms for large-scale approximate graph matching
Lubars, Joseph
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https://hdl.handle.net/2142/102401
Description
Title
Improving the output of algorithms for large-scale approximate graph matching
Author(s)
Lubars, Joseph
Issue Date
2018-09-13
Director of Research (if dissertation) or Advisor (if thesis)
Srikant, R.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Privacy
Social Networks
De-anonymization
Approximate Graph Matching
Stochastic Block Model
Abstract
In approximate graph matching, the goal is to find the best correspondence between the labels of two correlated graphs. Recently, the problem has been applied to social network de-anonymization, and several efficient algorithms have been proposed for approximate graph matching in that domain. These algorithms employ seeds, or matches known before running the algorithm, as a catalyst to match the remaining nodes in the graph. We adapt the ideas from these seeded algorithms to develop a computationally efficient method for improving any given correspondence between two graphs. In our analysis of our algorithm, we show a new parallel between the seeded social network de-anonymization algorithms and existing optimization-based algorithms. When given a partially correct correspondence between two Erdos-Renyi graphs as input, we show that our algorithm can correct all errors with high probability. Furthermore, when applied to real-world social networks, we empirically demonstrate that our algorithm can perform graph matching accurately, even without using any seed matches.
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