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Related Experiment Video

Updated: Mar 16, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

A perturbation-recovery generative autoencoder for heterogeneous graphs with attributes missing.

Quan Wang¹, Xinru Shao², Xiaodi Huang³

¹Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321000, China.

Scientific Reports

|March 15, 2026

Summary

This summary is machine-generated.

Heterogeneous Graph Generative Autoencoder (HGGAE) tackles missing node attributes by modeling missingness as a perturbation. This approach improves representation learning and downstream task performance on heterogeneous graphs.

Keywords:

Adversarial training Attribute missing Graph autoencoder Heterogeneous graph

Related Experiment Videos

Last Updated: Mar 16, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Area of Science:

Graph Machine Learning
Data Science
Artificial Intelligence

Background:

Heterogeneous graphs are crucial for social networks, recommendations, and bioinformatics.
Missing or corrupted node attributes degrade graph representation quality and task performance.
Existing methods struggle with attribute uncertainty and complex multi-relational dependencies.

Purpose of the Study:

To propose HGGAE (Heterogeneous Graph Generative Autoencoder), a novel generative autoencoder framework.
To address challenges of missing and corrupted attributes in heterogeneous graphs.
To improve representation learning and downstream task performance.

Main Methods:

HGGAE employs a perturbation-recovery paradigm for attribute restoration.
It uses a schedulable noise generator and relation-specific structural perturbation modules.
Adaptive perturbation intensity and sparse-target objectives enhance training efficiency.

Main Results:

HGGAE demonstrates strong performance in node classification on benchmark datasets.
Achieved significant Macro-F1 and Micro-F1 gains on the IMDB dataset (up to 7.8% and 8.5%).
Showcased competitive or superior results on Yelp, ACM, and DBLP datasets.

Conclusions:

HGGAE effectively models attribute missingness and improves representation learning.
The framework exhibits robustness and generalization capabilities in attribute-missing scenarios.
HGGAE offers a promising solution for real-world heterogeneous graph analysis.