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Prototype-Driven Multi-View Attribute-Missing Graph Clustering
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DOI：10.1109/TMM.2025.3613153.png)
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Abstract
En 中文
Attribute-missing deep graph clustering, which aims to categorize the graph nodes with partial attribute-missing samples into distinct categories in an unsupervised manner, has gained significant popularity. However, most existing researches have at least one of the following issues: 1) seldom exploit diverse clustering structural information to facilitate non-Euclidean data imputation and refine the clustering pattern and 2) ignoring the positive effect of diverse information on feature imputation and representation extraction, resulting in sub-optimal missing feature estimation and inferior clustering performance. To solve these issues, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b>rototype-driven <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b>ulti-view <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>ttribute-missing <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b>lustering (PMAGC) model that leverages rich structural and diverse information to assist the processes of imputing missing attributes and learning clustering-friendly features. Specifically, we design a multi-view augmentation module that extracts attribute-complete samples as node view and constructs feature and edge views using feature pre-imputation and edge masking techniques. Then, guided by clustering pseudo-labels, we promote the proximity between the prototypes of attribute-missing samples and those of attribute-complete samples within the feature space. Thus, PMAGC cleverly employs both clustering structural information and reliably attribute-complete sample data to assist feature imputation. In addition, we design a prototype-wise contrastive loss, which considers prototypes from different views within the same cluster as positive samples, while treating others as negative samples. Hence, the optimized features could more accurately guide the attribute learning process. Extensive experiments on six graph datasets with missing attributes are conducted to demonstrate the effectiveness of the proposed PMAGE.
Keywords:
Attribute-missing graph
deep graph clustering
feature completion
multi-view clustering
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