# Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability.

@article{Patsolic2014SeededGM, title={Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability.}, author={Heather G. Patsolic and Sancar Adali and Joshua T. Vogelstein and Youngser Park and Carey E. Friebe and Gongkai Li and Vince Lyzinski}, journal={arXiv: Machine Learning}, year={2014} }

We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated data examples, we demonstrate the versatility of our algorithm in matching graphs with various characteristics--weightedness, directedness, loopiness, many-to-one and many-to-many…

## 23 Citations

Tractable Graph Matching via Soft Seeding

- Computer Science
- 2018

It is shown that given certain properties of this initial matrix, with high probability the FAQ algorithm will converge in two steps to the truth under a flexible model for pairs of random graphs, implying that there will be no local optima near the global optima, providing a method to assess performance.

Information Recovery in Shuffled Graphs via Graph Matching

- Computer ScienceIEEE Transactions on Information Theory
- 2018

An information theoretic foundation is provided for understanding the practical impact that errorfully observed vertex correspondences can have on subsequent inference, and the capacity of graph matching methods to recover the lost vertex alignment and inferential performance.

Graph Matching via Multi-Scale Heat Diffusion

- Computer Science2019 IEEE International Conference on Big Data (Big Data)
- 2019

A novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations that incorporate both direct adjacencies as well as local structures induced from the heat diffusion is proposed.

Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks

- Mathematics, Computer ScienceJ. Comput. Graph. Stat.
- 2021

A corrupting channel model is introduced, and it is shown that in this model framework, the solution to the graph matching problem is a maximum likelihood estimator (MLE), as well as a relaxed notion of consistency in which a negligible fraction of the vertices need not be matched correctly.

Attributed Graph Matching via Seeds Guiding

- Computer ScienceIEEJ Transactions on Electrical and Electronic Engineering
- 2021

The approach introduces seed nodes to guide attributed graph matching, and considers explicitly the first‐order characteristics difference in the problem formulation, and outperforms original SGM, RGM and AGMLG significantly.

Multiplex graph matching matched filters

- Computer Science2019 IEEE International Conference on Big Data (Big Data)
- 2019

This work utilizes a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches in a larger multiplex background network.

A Short Survey of Recent Advances in Graph Matching

- Computer ScienceICMR
- 2016

The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.

Clustered Graph Matching for Label Recovery and Graph Classification

- Computer ScienceArXiv
- 2022

It is demonstrated both in theory and practice that if the graphs come from different network classes, then clustering the networks into classes followed by matching the new graph to cluster-averages can yield higher reputation matching performance than matching to the global average graph.

Robust Estimation from Multiple Graphs under Gross Error Contamination

- Computer Science
- 2017

It is demonstrated that, under appropriate conditions, the estimator both maintains Lq robustness and wins the bias-variance tradeoff by exploiting low-rank graph structure.

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