Graph Clustering with Masked Autoencoders: A Novel Framework for Efficient and Generalized Graph Clustering

Graph clustering algorithms have gained significant attention in recent years due to their ability to reveal meaningful structures in complex networks. One popular approach is using autoencoder structures, which have shown promising results in terms of performance and training cost. However, existing graph autoencoder clustering algorithms based on Graph Convolutional Networks (GCN) or Graph Attention Networks (GAT) face some limitations.

The first limitation is the lack of good generalization ability. These algorithms often struggle to perform well on unseen data or datasets with different characteristics. This hinders their practical application in real-world scenarios where datasets may vary from those encountered during training.

The second limitation is the difficulty in determining the number of clusters automatically. Existing autoencoder models typically require this information to be provided by the user, which may not always be possible or practical. Therefore, there is a need for a framework that can overcome these limitations.

To address these challenges, the proposed framework called Graph Clustering with Masked Autoencoders (GCMA) introduces a novel fusion autoencoder based on the graph masking method. This fusion autoencoder performs the fusion coding of the graph, enabling the model to capture more generalized and comprehensive knowledge about the underlying graph structure.

In addition, GCMA incorporates an improved density-based clustering algorithm as a second decoder during decoding with multi-target reconstruction. This algorithm helps to improve the generalization ability of the model and enables end-to-end output of the number of clusters and clustering results.

Furthermore, GCMA is a nonparametric class method, meaning that it does not require any assumptions about the underlying distribution of the data. This makes it more flexible and robust in handling different types of graphs and clustering tasks.

Extensive experiments have been conducted to evaluate the performance of GCMA against state-of-the-art baselines. The results demonstrate the superiority of GCMA in terms of clustering accuracy, robustness, and scalability.

Expert Analysis: Improving Generalization Ability and Automating Clustering

The proposed GCMA framework addresses two critical issues in graph autoencoder clustering algorithms. By introducing the fusion autoencoder and the improved density-based clustering algorithm, it aims to enhance the generalization ability of the model, allowing it to perform well on unseen data. This is crucial for real-world applications where datasets may exhibit different characteristics over time.

Moreover, the automatic determination of the number of clusters is a significant advancement. The traditional approach of manually specifying this parameter can be time-consuming and subjective. With GCMA, users can obtain the number of clusters and clustering results end-to-end, without the need for prior knowledge or user intervention. This automation greatly improves the practicality and usability of the framework.

Another notable aspect of GCMA is its nonparametric nature. By not assuming any specific distribution for the data, GCMA can handle various types of graphs, making it more versatile and adaptable. This is particularly valuable in scenarios where the underlying graph structure may not be well-defined or follows a non-standard pattern.

In conclusion, GCMA represents an innovative approach to graph clustering with autoencoder structures. Its fusion autoencoder, improved density-based clustering algorithm, and end-to-end calculation of the number of clusters make it a valuable tool in exploring and understanding complex network structures.

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