research-article

Semi-supervised Multi-label Learning for Graph-structured Data

Authors:

Irwin KingAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 1723 - 1733

https://doi.org/10.1145/3459637.3482391

Published: 30 October 2021 Publication History

Abstract

The semi-supervised multi-label classification problem primarily deals with Euclidean data, such as text with a 1D grid of tokens and images with a 2D grid of pixels. However, the non-Euclidean graph-structured data naturally and constantly appears in semi-supervised multi-label learning tasks from various domains like social networks, citation networks, and protein-protein interaction (PPI) networks. Moreover, the existing popular node embedding methods, like Graph Neural Networks (GNN), focus on graphs with simplex labels and tend to neglect label correlations in the multi-label setting, so the easy adaption proves empirically ineffective. Therefore, graph representation learning for the semi-supervised multi-label learning task is crucial and challenging. In this work, we incorporate the idea of label embedding into our proposed model to capture both network topology and higher-order multi-label correlations. The label embedding is generated along with the node embedding based on the topological structure to serve as the prototype center for each class. Moreover, the similarity of the label embedding and node embedding can be used as a confidence vector to guide the label smoothing process, formulating as a margin ranking optimization problem to learn the second-order relations between labels. Extensive experiments on real-world datasets from various domains demonstrate that our model significantly outperforms the state-of-the-art models for node-level tasks.

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Index Terms

Semi-supervised Multi-label Learning for Graph-structured Data
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings
    2. Machine learning approaches
      1. Learning latent representations

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
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Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
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Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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November 1 - 5, 2021

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https://dl.acm.org/doi/10.5555/3692070.3693375
Zhang YZhu HSong ZChen YFu XMeng ZKoniusz PKing IBaeza-Yates RBonchi F(2024)Geometric View of Soft Decorrelation in Self-Supervised LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671914(4338-4349)Online publication date: 25-Aug-2024
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Jing PZhao XFan FYang FLi YSu Y(2024)Multimodal Progressive Modulation Network for Micro-Video Multi-Label ClassificationIEEE Transactions on Multimedia10.1109/TMM.2024.340572426(10134-10144)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3405724
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