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Learning Graph Edit Distance by Graph Neural Networks [article]

Pau Riba, Andreas Fischer, Josep Lladós, Alicia Fornés
2020 arXiv   pre-print
Our method employs a message passing neural 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.  ... 
arXiv:2008.07641v1 fatcat:gqq6fmwkqbf4zlkzk263cyahsm

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits [article]

Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Dąbrowski-Tumański, Mikołaj Chromiński, Rafał Loska, Paweł Włodarczyk-Pruszyński, Stanisław Jastrzębski
2021 arXiv   pre-print
With this in mind, we present Molecule 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.  ... 
arXiv:2006.15426v2 fatcat:kjalk4ccpfhw3i66egbuphgpwm

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing [article]

Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, Yiyang Lu
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.  ... 
arXiv:2201.03681v3 fatcat:wmqrd3e7qbeoxglzjmncx4wydm

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

Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, Wei Wang
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.  ... 
arXiv:1808.05689v4 fatcat:g5or3q6tanae3jerfjpikohzta

Graph and Network Algorithms [chapter]

Balaji Raghavachari
2014 Computing Handbook, Third Edition  
The book 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.  ... 
doi:10.1201/b16812-8 fatcat:efokpyhf6zdzblbosyee6uaem4

EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks [article]

Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li
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.  ... 
arXiv:2108.05233v1 fatcat:ii34gu3c3bhtjj4nuyjmq63pte

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

Nadeem Iqbal Kajla, Malik Muhammad Saad Missen, Muhammad Muzzamil Luqman., Mickael Coustaty, Arif Mehmood, Gyu Sang Choi.
2020 IEEE Access  
We compare traditional Graph Edit Distance methods with Graph Neural Networks.  ...  graph edit distance.  ... 
doi:10.1109/access.2020.3035886 fatcat:5agu4l3mtzfsxcx5vgvedqloe4

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks [chapter]

Paul Maergner, Vinaychandran Pondenkandath, Michele Alberti, Marcus Liwicki, Kaspar Riesen, Rolf Ingold, Andreas Fischer
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.  ... 
doi:10.1007/978-3-319-97785-0_45 fatcat:rwxagaex25hehic3mr3jfjgf7a

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

Kelly Marie Kapsabelis, Peter John Dickinson, Kutluyil Dogancay
2007 ANZIAM Journal  
A recent novel approach represents the logical communications of a periodically observed 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] .  ... 
doi:10.21914/anziamj.v48i0.47 fatcat:i4iwqlzknra5nohunjp6gxf2ze

Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

Prasoon Kumar Vinodkumar, Cagri Ozcinar, Gholamreza Anbarjafari
2021 Entropy  
CRISPR/Cas9 is a powerful genome-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  ... 
doi:10.3390/e23050608 pmid:34069050 fatcat:ntnmwns2bvhofdjuqtkq3o6d3q

A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance [chapter]

Xavier Cortés, Donatello Conte, Hubert Cardot, Francesc Serratosa
2018 Lecture Notes in Computer Science  
The aim of this paper is to present a model to compute the assignments costs for the 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.  ... 
doi:10.1007/978-3-319-97785-0_31 fatcat:4qojcp3mz5didfbduobp4mx4oa

Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching [article]

Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang
2021 arXiv   pre-print
, compared to existing popular methods for approximate 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.  ... 
arXiv:1809.04440v2 fatcat:cczq7fqu6vatdf5lyu326sgl3q

Inferring coarse views of connectivity in very large graphs

Reza Motamedi, Reza Rejaie, Walter Willinger, Daniel Lowd, Roberto Gonzalez
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 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.  ... 
doi:10.1145/2660460.2660480 dblp:conf/cosn/MotamediRWLG14 fatcat:uldqmlgwyjerdevtdca7q52lpm

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing [article]

Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, Yiyang Lu
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.  ... 
doi:10.48550/arxiv.2201.03681 fatcat:rp2sl4ndivhvnmuogztal7bu5i

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

Rashid Ibragimov, Maximilian Malek, Jiong Guo, Jan Baumbach
unpublished
Underlying our approach is the so-called 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.  ... 
fatcat:6kycnbmppndftjzg2qrmllhzze
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