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Related Experiment Video

Updated: May 8, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Contrastive graph clustering with Structure-Robust learning and stable prototype guidance.

Yuanfu Ding1, Xiufang Xu1, Sen Xu2

  • 1School of Information Engineering, Yancheng Institute of Technology, Yancheng, 224051, China; Jiangsu Provincial Engineering Technology Center for Multimodal Perception and Intelligent Control of Offshore Wind Power Systems, Yancheng, 224051, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for contrastive graph clustering, enhancing node representation learning by improving feature importance and training stability. The proposed method, SR-SPG, effectively addresses challenges in high-dimensional attributed graphs.

Keywords:
Attributed graph clusteringContrastive graph clusteringNode-adaptive feature gatingSelf-supervised learningStable prototype guidance

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Related Experiment Videos

Last Updated: May 8, 2026

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Published on: March 18, 2019

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Graph Neural Networks

Background:

  • Contrastive graph clustering is vital for unsupervised node representation learning.
  • High-dimensional attributed graphs present challenges like ignored node-wise feature importance and unstable training targets.

Purpose of the Study:

  • To propose a unified framework, SR-SPG, to address limitations in contrastive graph clustering.
  • To enhance inter-cluster separability and training stability in high-dimensional attributed graphs.

Main Methods:

  • SR-SPG employs node-adaptive feature gating by integrating neighborhood consistency, Fisher-discriminative, and adaptive learning priors.
  • Temporally smoothed self-training targets are constructed using EMA-updated prototypes for improved stability.

Main Results:

  • The proposed SR-SPG framework enhances feature separability and training stability.
  • Experiments on six benchmark datasets confirm the effectiveness and robustness of SR-SPG.

Conclusions:

  • SR-SPG offers a robust solution for contrastive graph clustering in high-dimensional attributed graphs.
  • The framework improves node representation learning by addressing feature importance and temporal consistency issues.