Open-World Semi-Supervised Learning for Node Classification

Published in

IEEE International Conference on Data Engineering(2024)

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TL;DR

This paper proposes an IMbalance-A ware method named OpenIMA for Open-world semi-supervised node classification, which trains the node classification model from scratch via contrastive learning with bias-reduced pseudo labels.

Abstract

Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community. As only seen classes have human labels, they are usually better learned than novel classes, and thus exhibit smaller intra-class variances within the embedding space (named as imbalance of intra-class variances between seen and novel classes). Based on empirical and theoretical analysis, we find the variance imbalance can negatively impact the model performance. Pre-trained feature encoders can alleviate this issue via producing compact representations for novel classes. However, creating general pre-trained encoders for various types of graph data has been proven to be challenging. As such, there is a demand for an effective method that does not rely on pre-trained graph encoders. In this paper, we propose an IMbalance-A ware method named OpenIMA for Open-world semi-supervised node classification, which trains the node classification model from scratch via contrastive learning with bias-reduced pseudo labels. Extensive experiments on seven popular graph benchmarks demonstrate the effectiveness of OpenIMA, and the source code has been available on GitHub11https://github.com/RUCKBReasoning/OpenIMA.

Authors

Jing Zhang

2 papers

Cuiping Li

2 papers

Yanling Wang

1 papers

References70 items

Contrastive Pseudo Learning for Open-World DeepFake Attribution

G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

Open-World Graph Active Learning for Node Classification

All in One: Multi-Task Prompting for Graph Neural Networks

Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery

Open-World Semi-Supervised Learning for Node Classification

Published in

IEEE International Conference on Data Engineering(2024)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Jing Zhang

2 papers

Cuiping Li

2 papers

Yanling Wang

1 papers

References70 items

Contrastive Pseudo Learning for Open-World DeepFake Attribution

G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

Open-World Graph Active Learning for Node Classification

All in One: Multi-Task Prompting for Graph Neural Networks

Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery

Lingxi Zhang

1 papers

Yuxiao Dong

1 papers

Hongzhi Yin

1 papers

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Field of Study

Computer Science

Journal Information

Name

2024 IEEE 40th International Conference on Data Engineering (ICDE)

Page

2723-2736

Venue Information

Name

IEEE International Conference on Data Engineering

Type

conference

URL

http://www.wikicfp.com/cfp/program?id=1331

Alternate Names

ICDE
Int Conf Data Eng
IEEE Int Conf Data Eng
International Conference on Data Engineering