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Learning Graph Edit Distance by Graph Neural Networks
[article]

2020
*
arXiv
*
pre-print

Our method employs a message passing neural

arXiv:2008.07641v1
fatcat:gqq6fmwkqbf4zlkzk263cyahsm
*network*to capture the*graph*structure, and thus, leveraging this information for its use on a distance computation. ... On the one hand, in a*graph*retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate)*graph**edit*distance benchmarks. ... Section 2 introduces the related work on*graph*neural*networks*. Section 3 introduce the*graph**edit*distance algorithm along with relevant approximations. ...##
###
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
[article]

2021
*
arXiv
*
pre-print

With this in mind, we present Molecule

arXiv:2006.15426v2
fatcat:kjalk4ccpfhw3i66egbuphgpwm
*Edit**Graph*Attention*Network*(MEGAN), an end-to-end encoder-decoder neural model. ... MEGAN is inspired by models that express a chemical reaction as a sequence of*graph**edits*, akin to the arrow pushing formalism. ... In this work, we present the Molecule*Edit**Graph*Attention*Network*(MEGAN). We propose an encoder-decoder model that generates a reaction as a sequence of*graph**edits*. ...##
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FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
[article]

2022
*
arXiv
*
pre-print

*Graph*Neural

*Networks*(GNNs) have proven to excel in predictive modeling tasks where the underlying data is a

*graph*. ... FairEdit performs efficient edge

*editing*by leveraging gradient information of a fairness loss to find edges that improve fairness. ... GCN (

*Graph*Convolution

*Network*) [10] proposed a

*graph*representation and propagation rule for convolution neural

*network*to be applied on

*graph*-structured data. ...

##
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SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
[article]

2020
*
arXiv
*
pre-print

*Graph*similarity computation, such as

*Graph*

*Edit*Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of

*graph*similarity search and many other applications, but very costly to compute ... Inspired by the recent success of neural

*network*approaches to several

*graph*applications, such as node or

*graph*classification, we propose a novel neural

*network*based approach to address this classic ...

*graph*

*edit*operations. ...

##
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Graph and Network Algorithms
[chapter]

2014
*
Computing Handbook, Third Edition
*

The book

doi:10.1201/b16812-8
fatcat:efokpyhf6zdzblbosyee6uaem4
*edited*by Hochbaum [1996] provides an extensive coverage of work on approximation algorithms for*graph*problems. Conferences and Current Work. ... A class of important problems in*graphs*are shortest-path problems, which play an important role in routing messages efficiently in*networks*. ...##
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EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
[article]

2021
*
arXiv
*
pre-print

*Graph*Neural

*Networks*(GNNs) have recently demonstrated superior capability of tackling

*graph*analytical problems in various applications. ... Based on the optimization objective, we develop a framework named

*EDITS*to mitigate the bias in attributed

*networks*while preserving useful information. ... INTRODUCTION

*Graph*-structured data, such as social

*networks*[34, 52] , chemical reaction

*networks*[13] and traffic

*networks*[49] , has become ubiquitous in a plethora of realms. ...

##
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Additive Angular Margin Loss in Deep Graph Neural Network Classifier For Learning Graph Edit Distance

2020
*
IEEE Access
*

We compare traditional

doi:10.1109/access.2020.3035886
fatcat:5agu4l3mtzfsxcx5vgvedqloe4
*Graph**Edit*Distance methods with*Graph*Neural*Networks*. ...*graph**edit*distance. ...##
###
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
[chapter]

2018
*
Lecture Notes in Computer Science
*

*networks*. ... In this work, we propose to complement a recent structural approach to offline signature verification based on

*graph*

*edit*distance with a statistical approach based on metric learning with deep neural ... the

*graph*

*edit*distance. ...

##
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Investigation of graph edit distance cost functions for detection of network anomalies

2007
*
ANZIAM Journal
*

A recent novel approach represents the logical communications of a periodically observed

doi:10.21914/anziamj.v48i0.47
fatcat:i4iwqlzknra5nohunjp6gxf2ze
*network*as a time series of*graphs*and applies the*graph*matching technique,*graph**edit*distance, to monitor and ... To date, only simple cost functions for*graph**edit*operations have been used in application to computer*network*monitoring. This article investigates simple See ...*Graph**edit*distance In computer*networks*, a number of*graph*distance measures have been applied to a time series of*graphs*to investigate*network*behaviour over time [5] . ...##
###
Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

2021
*
Entropy
*

CRISPR/Cas9 is a powerful genome-

doi:10.3390/e23050608
pmid:34069050
fatcat:ntnmwns2bvhofdjuqtkq3o6d3q
*editing*technology that has been widely applied in targeted gene repair and gene expression regulation. ... In this research work, we introduce a novel*graph*-based approach to predict off-target efficacy of sgRNA in the CRISPR/Cas9 system that is easy to understand and replicate for researchers. ... GCN, a powerful*graph*neural*network*model used for performing representation learning of*graphs*, can predict off-target efficacy in CRISPR/Cas9 gene*editing*by performing link prediction on the off-target ...##
###
A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance
[chapter]

2018
*
Lecture Notes in Computer Science
*

The aim of this paper is to present a model to compute the assignments costs for the

doi:10.1007/978-3-319-97785-0_31
fatcat:4qojcp3mz5didfbduobp4mx4oa
*Graph**Edit*Distance by means of a Deep Neural*Network*previously trained with a set of pairs of*graphs*properly matched ... The*Graph**Edit*Distance is one of the most popular approaches to solve this problem. This method needs to define a set of parameters and the cost functions aprioristically. ... We consider that this work represents an important step to define the costs functions for node assignments in the problem of the*Graph**Edit*Distance. ...##
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Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
[article]

2021
*
arXiv
*
pre-print

, compared to existing popular methods for approximate

arXiv:1809.04440v2
fatcat:cczq7fqu6vatdf5lyu326sgl3q
*Graph**Edit*Distance (GED) and Maximum Common Subgraph (MCS) computation. ... Recently, several data-driven approaches based on neural*networks*have been proposed, most of which model the*graph*-*graph*similarity as the inner product of their*graph*-level representations, with different ... CNN based*graph*matching method to other*graph*matching tasks, e.g.*network*alignment, as well as the explicit generation of*edit*sequence for GED and node correspondence for MCS. ...##
###
Inferring coarse views of connectivity in very large graphs

2014
*
Proceedings of the second edition of the ACM conference on Online social networks - COSN '14
*

Leveraging this indirect sign of connectivity enables our proposed framework to effectively scale with

doi:10.1145/2660460.2660480
dblp:conf/cosn/MotamediRWLG14
fatcat:uldqmlgwyjerdevtdca7q52lpm
*graph*size. ... We leverage the transient behavior of many short random walks (RW) on a large*graph*that is assumed to have regions of varying edge density but whose structure is otherwise unknown. ... Miles Nerenberg has packaged our research prototype into a publicly available interactive tool for exploring coarse views of large*graphs*. ...##
###
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
[article]

2022

*Graph*Neural

*Networks*(GNNs) have proven to excel in predictive modeling tasks where the underlying data is a

*graph*. ... FairEdit performs efficient edge

*editing*by leveraging gradient information of a fairness loss to find edges that improve fairness. ... GCN (

*Graph*Convolution

*Network*) [10] proposed a

*graph*representation and propagation rule for convolution neural

*network*to be applied on

*graph*-structured data. ...

##
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GEDEVO: An Evolutionary Graph Edit Distance Algorithm for Biological Network Alignment

unpublished

Underlying our approach is the so-called

fatcat:6kycnbmppndftjzg2qrmllhzze
*Graph**Edit*Distance (GED) model, where one*graph*is to be transferred into another one, with a minimal number of (or more general: minimal costs for) edge insertions ... The emerging interaction*networks*are usually modeled as*graphs*with thousands of nodes and tens of thousands of edges between them. ... Termination No practical exact algorithm for the*Graph**Edit*Distance computation on large*graphs*exists. ...
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