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A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery.

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Summary

This study introduces a dual-masked deep structural clustering network (DMDSC) to improve graph clustering. DMDSC addresses feature corruption and network degradation, outperforming existing deep clustering methods.

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Area of Science:

Artificial Intelligence
Machine Learning
Graph Neural Networks

Background:

Structured clustering networks use autoencoders (AE) and graph convolutional networks (GCNs) to mitigate oversmoothing.
Existing methods suffer from learning representations without considering feature/structure corruption and face network degradation/vanishing gradients.

Purpose of the Study:

To propose a novel deep clustering method, the dual-masked deep structural clustering network (DMDSC).
To address limitations in current structured clustering networks, specifically feature/structure corruption and network degradation issues.

Main Methods:

DMDSC employs generative self-supervised learning for reconstructing corrupted structures and features, mining deeper correlations.
An adaptive bidirectional information delivery (ABID) module is developed to create an information transfer channel between AE and GCN layers.
The ABID module aims to alleviate oversmoothing and vanishing gradient problems in deep GCNs.

Main Results:

Experiments on six benchmark datasets demonstrate the effectiveness of the proposed DMDSC.
DMDSC significantly outperforms state-of-the-art deep clustering algorithms.

Conclusions:

DMDSC offers a robust solution for deep graph clustering by tackling feature corruption and network degradation.
The adaptive bidirectional information delivery mechanism effectively enhances network performance and stability.