Data Quality-aware Graph Machine Learning

Yu Wang; Kaize Ding; Xiaorui Liu; Jian Kang; Ryan Rossi; Tyler Derr

doi:10.1145/3627673.3679095

Data Quality-aware Graph Machine Learning

Yu Wang, Kaize Ding, Xiaorui Liu, Jian Kang, Ryan Rossi, Tyler Derr

Statistics and Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Recent years have seen a significant shift in Artificial Intelligence from model-centric to data-centric approaches, highlighted by the success of large foundational models. Following this trend, despite numerous innovations in graph machine learning model design, graph-structured data often suffers from data quality issues, jeopardizing the progress of Data-centric AI in graph-structured applications. Our proposed tutorial addresses this gap by raising awareness about data quality issues within the graph machine-learning community. We provide an overview of existing topology, imbalance, bias, limited data, and abnormality issues in graph data. Additionally, we highlight recent developments in foundational graph models that focus on identifying, investigating, mitigating, and resolving these issues.

Original language	English (US)
Title of host publication	CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	5534-5537
Number of pages	4
ISBN (Electronic)	9798400704369
DOIs	https://doi.org/10.1145/3627673.3679095
State	Published - Oct 21 2024
Event	33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 - Boise, United States Duration: Oct 21 2024 → Oct 25 2024

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
ISSN (Print)	2155-0751

Conference

Conference	33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Country/Territory	United States
City	Boise
Period	10/21/24 → 10/25/24

Funding

This research is supported by the National Science Foundation (NSF) under grant number IIS2239881 and Adobe Research.

Keywords

data-centric artificial intelligence
graph machine learning

ASJC Scopus subject areas

General Business, Management and Accounting
General Decision Sciences

Access to Document

10.1145/3627673.3679095

Cite this

Wang, Y., Ding, K., Liu, X., Kang, J., Rossi, R., & Derr, T. (2024). Data Quality-aware Graph Machine Learning. In CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 5534-5537). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3627673.3679095

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author = "Yu Wang and Kaize Ding and Xiaorui Liu and Jian Kang and Ryan Rossi and Tyler Derr",

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language = "English (US)",

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Wang, Y, Ding, K, Liu, X, Kang, J, Rossi, R & Derr, T 2024, Data Quality-aware Graph Machine Learning. in CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 5534-5537, 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, United States, 10/21/24. https://doi.org/10.1145/3627673.3679095

Data Quality-aware Graph Machine Learning. / Wang, Yu; Ding, Kaize; Liu, Xiaorui et al.
CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2024. p. 5534-5537 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Rossi, Ryan

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AB - Recent years have seen a significant shift in Artificial Intelligence from model-centric to data-centric approaches, highlighted by the success of large foundational models. Following this trend, despite numerous innovations in graph machine learning model design, graph-structured data often suffers from data quality issues, jeopardizing the progress of Data-centric AI in graph-structured applications. Our proposed tutorial addresses this gap by raising awareness about data quality issues within the graph machine-learning community. We provide an overview of existing topology, imbalance, bias, limited data, and abnormality issues in graph data. Additionally, we highlight recent developments in foundational graph models that focus on identifying, investigating, mitigating, and resolving these issues.

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M3 - Conference contribution

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Data Quality-aware Graph Machine Learning

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Keywords

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