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Towards Data-centric Graph Machine Learning: Review and Outlook

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arxiv 2309.10979 v1 pith:3F7EI3LS submitted 2023-09-20 cs.LG

Xin Zheng , Yixin Liu , Zhifeng Bao , Meng Fang , Xia Hu , Alan Wee-Chung Liew , Shirui Pan This is my paper · ORCID
classification cs.LG
keywords graphdatadata-centricapplicationscollectiondc-gmllearningmachine
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Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years. In this article, we conduct an in-depth and comprehensive review, offering a forward-looking outlook on the current efforts in data-centric AI pertaining to graph data-the fundamental data structure for representing and capturing intricate dependencies among massive and diverse real-life entities. We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of the graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance. A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-centric view. Lastly, we pinpoint the future prospects of the DC-GML domain, providing insights to navigate its advancements and applications.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Graph Data Management and Graph Machine Learning: Synergies and Opportunities

    cs.DB 2025-02 conditional novelty 4.0 of 10

    This survey reviews the two-way synergy between graph data management and graph machine learning, organized as a pipeline of cleaning, embedding, training, indexing, explanation, and query answering.

  2. GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification

    cs.LG 2024-11 conditional novelty 4.0 of 10

    GNN-MultiFix combines graph features, propagated training labels, and DeepWalk position embeddings to improve multi-label node classification.

  3. Towards Data-centric Machine Learning on Directed Graphs: a Survey

    cs.LG 2024-11 unverdicted novelty 3.0 of 10

    A survey taxonomizing directed graph neural networks into message-passing, eigenpolynomial, and sequence-based frameworks and re-reading them from a data-centric perspective.

  4. Understanding Graph Databases: A Comprehensive Tutorial and Survey

    cs.DB 2024-11 unverdicted

    A tutorial and survey of graph databases and graph algorithms that compiles existing material but contains several incorrect code outputs.

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