tutorial

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Graph Minimally-supervised Learning

Authors:

Huan LiuAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 1620 - 1622

https://doi.org/10.1145/3488560.3501390

Published: 15 February 2022 Publication History

Abstract

Graphs are widely used for abstracting complex systems of interacting objects, such as social networks, knowledge graphs, and traffic networks, as well as for modeling molecules, manifolds, and source code. To model such graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. Prevailing graph learning methods usually rely on learning from "big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with "small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning. We believe this tutorial is beneficial to researchers and practitioners, allowing them to collaborate on graph learning.

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Cited By

Liu YDing KWang JLee VLiu HPan SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Learning Strong Graph Neural Networks with Weak InformationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599410(1559-1571)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599410
Wang SDong YDing KChen CLi JChua TLauw HSi LTerzi ETsaparas P(2023)Few-shot Node Classification with Extremely Weak SupervisionProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570435(276-284)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570435

Index Terms

Graph Minimally-supervised Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

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Published: 15 February 2022

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WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining

February 21 - 25, 2022

AZ, Virtual Event, USA

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Cited By

Liu YDing KWang JLee VLiu HPan SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Learning Strong Graph Neural Networks with Weak InformationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599410(1559-1571)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599410
Wang SDong YDing KChen CLi JChua TLauw HSi LTerzi ETsaparas P(2023)Few-shot Node Classification with Extremely Weak SupervisionProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570435(276-284)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570435

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