The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs Article Swipe

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Christopher Morris , Matthias Fey , Nils M. Kriege ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.24963/ijcai.2021/618

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine learning setting. We discuss the theoretical background, show how to use it for supervised graph- and node classification, discuss recent extensions, and its connection to neural architectures. Moreover, we give an overview of current applications and future directions to stimulate research.

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Computer Science

Artificial Intelligence

Machine Learning

Heuristic

Algorithm

Theoretical Computer Science

Line Graph

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Computer science Artificial intelligence Machine learning Graph isomorphism Heuristic Graph Artificial neural network Algorithm Theoretical computer science Line graph

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Type: preprint
Language: en
Landing Page: https://doi.org/10.24963/ijcai.2021/618
PDF: https://www.ijcai.org/proceedings/2021/0618.pdf
OA Status: gold
Cited By: 3
References: 93
Related Works: 20
OpenAlex ID: https://openalex.org/W3161822059

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DOI: https://doi.org/10.24963/ijcai.2021/618

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Title: The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-08-01

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Authors: Christopher Morris, Matthias Fey, Nils M. Kriege

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Landing page: https://doi.org/10.24963/ijcai.2021/618

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Concepts: Computer science, Artificial intelligence, Machine learning, Graph isomorphism, Heuristic, Graph, Artificial neural network, Algorithm, Theoretical computer science, Line graph

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Cited by: 3

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Citations by year (recent): 2025: 1, 2022: 2

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abstract_inverted_index.recent	1, 66
abstract_inverted_index.years,	2
abstract_inverted_index.current	80
abstract_inverted_index.discuss	50, 65
abstract_inverted_index.emerged	20
abstract_inverted_index.machine	27, 46
abstract_inverted_index.learning	28, 47
abstract_inverted_index.overview	39, 78
abstract_inverted_index.powerful	23
abstract_inverted_index.problem,	19
abstract_inverted_index.setting.	48
abstract_inverted_index.Moreover,	74
abstract_inverted_index.heuristic	14
abstract_inverted_index.research.	87
abstract_inverted_index.stimulate	86
abstract_inverted_index.algorithm,	11
abstract_inverted_index.algorithms	3
abstract_inverted_index.connection	70
abstract_inverted_index.directions	84
abstract_inverted_index.relational	32
abstract_inverted_index.supervised	60
abstract_inverted_index.well-known	13
abstract_inverted_index.algorithm's	42
abstract_inverted_index.background,	53
abstract_inverted_index.extensions,	67
abstract_inverted_index.isomorphism	18
abstract_inverted_index.theoretical	52
abstract_inverted_index.(supervised)	26
abstract_inverted_index.applications	81
abstract_inverted_index.architectures	6
abstract_inverted_index.comprehensive	38
abstract_inverted_index.architectures.	73
abstract_inverted_index.classification,	64
abstract_inverted_index.Weisfeiler-Leman	10
cited_by_percentile_year.max	96
cited_by_percentile_year.min	91
countries_distinct_count	3
institutions_distinct_count	3
citation_normalized_percentile.value	0.62317031
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False