research-article

Zero-shot Node Classification with Decomposed Graph Prototype Network

Authors:

Yuchen Guo, and

Zhiguo GongAuthors Info & Claims

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

August 2021

Pages 1769 - 1779

https://doi.org/10.1145/3447548.3467230

Published: 14 August 2021 Publication History

Abstract

Node classification is a central task in graph data analysis. Scarce or even no labeled data of emerging classes is a big challenge for existing methods. A natural question arises: can we classify the nodes from those classes that have never been seen?

In this paper, we study this zero-shot node classification (ZNC) problem which has a two-stage nature: (1) acquiring high-quality class semantic descriptions (CSDs) for knowledge transfer, and (2) designing a well generalized graph-based learning model. For the first stage, we give a novel quantitative CSDs evaluation strategy based on estimating the real class relationships, to get the "best" CSDs in a completely automatic way. For the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN) method, following the principles of locality and compositionality for zero-shot model generalization. Finally, we conduct extensive experiments to demonstrate the effectiveness of our solutions.

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Cited By

Liang WHao ZLiu HChen H(2024)Boosting Zero-Shot Node Classification via Dependency Capture and Discriminative Feature LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446823(7360-7364)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446823
Wu LJiang JZhao HWang HLian DZhang MChen EElkind E(2023)KMFProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/262(2361-2369)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/262
Jiang JZhao HHe MWu LZhang KFan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614945(965-975)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614945
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Zero-shot Node Classification with Decomposed Graph Prototype Network

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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 ACM.

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Published: 14 August 2021

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National Natural Science Foundation of China
GSTIC
University of Macau
Fundamental Research Funds for the Central Universities
National Key D&R Program of China
Macao Youth Scholars Program
The Science and Technology Development Fund, Macau SAR

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

August 14 - 18, 2021

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Cited By

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https://doi.org/10.1109/ICASSP48485.2024.10446823
Wu LJiang JZhao HWang HLian DZhang MChen EElkind E(2023)KMFProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/262(2361-2369)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/262
Jiang JZhao HHe MWu LZhang KFan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614945(965-975)Online publication date: 21-Oct-2023
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