abstract

Data Efficient Learning on Graphs

Authors:

Meng JiangAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 4092 - 4093

https://doi.org/10.1145/3447548.3470813

Published: 14 August 2021 Publication History

Abstract

Prevailing methods of graph representation learning (GRL) 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 is always a time and resource consuming task. The fact overshadows GRL's capability and applicability for many real situations. Therefore, data efficient learning on graphs has become essential for many real-world applications and there have been many studies working on this topic in recent years. In this tutorial, we will systematically review recent studies of data efficient learning on graphs, in particular a series of methods and applications of graph few-shot learning and graph self-supervised learning. At first, we will introduce the overview of graph representation learning methods, conventional few-shot learning, and self-supervised learning techniques. Then, we will present the work of data efficient learning on graphs in terms of three major graph mining tasks at different granularity levels: node-level learning tasks, graph-level learning tasks, and edge-level learning tasks. In the end, we will conclude the tutorial and raise open problems and pressing issues in future research. The authors of this tutorial are active and productive researchers in this research area.

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Digital Library

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Cited By

Liu CYang YXie ZLu HDing YChua TLauw HSi LTerzi ETsaparas P(2023)Position-Aware Subgraph Neural Networks with Data-Efficient LearningProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570429(643-651)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570429

Index Terms

Data Efficient Learning on Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning

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Published In

cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 14 August 2021

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KDD '21

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Liu CYang YXie ZLu HDing YChua TLauw HSi LTerzi ETsaparas P(2023)Position-Aware Subgraph Neural Networks with Data-Efficient LearningProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570429(643-651)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570429

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