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Computing Graph Edit Distance via Neural Graph Matching
Chengzhi Piao
, Tingyang Xu
*
, Xiangguo Sun
, Yu Rong
, Kangfei Zhao
, Hong Cheng
*
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Conference article
›
peer-review
51
Citations (Scopus)
Overview
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Dive into the research topics of 'Computing Graph Edit Distance via Neural Graph Matching'. Together they form a unique fingerprint.
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Computer Science
Graph Matching
100%
Graph Neural Network
100%
Mean Absolute Error
66%
Similarity Score
66%
Machine Learning Model
66%
Learning Framework
33%
Deep Learning Method
33%
Searching Algorithm
33%
Greedy Algorithm
33%
Interpretability
33%
bipartite matching
33%
Postprocessing
33%
Processing Algorithm
33%
Graph Theory
33%
Regression Task
33%
Prediction Value
33%
Primitive Operation
33%
Keyphrases
Graph Edit Distance
100%
Neural Graph Matching
100%
Distance Parameter
33%
Best Matches
22%
Suboptimal Solutions
22%
Machine Learning Models
22%
Distance Computation
22%
Mean Absolute Error
22%
Graph Neural Network
22%
Similarity Score
22%
Matching Matrix
22%
Minimum number
11%
Deep Learning Framework
11%
Greedy Algorithm
11%
Search Methods
11%
Graph Structure
11%
Search Algorithm
11%
NP-hardness
11%
Classical Algorithm
11%
Graph Theory
11%
Value-based
11%
Bipartite Matching
11%
Numerical Precision
11%
Post-processing Algorithm
11%
NP-hard Problem
11%
Inexact Algorithm
11%
Distance Problems
11%
Node Matching
11%
Regression Task
11%
Value Prediction
11%
Beam Search Algorithm
11%
Operation Sequence
11%
Power-law Graphs
11%
Intrinsic Complexity
11%
K-best
11%
Primitive Operation
11%
Engineering
Distance Value
100%
Learning System
66%
Similarity
66%
Mean Absolute Error
66%
Deep Learning Method
33%
Greedy Algorithm
33%
Nodes
33%
Search Algorithm
33%
Processing Algorithm
33%
Primitive Operation
33%
Graph Theory
33%
Interpretability
33%