Structural Deep Clustering Network (2002.01633v3)

Abstract: Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning, e.g., autoencoder, suggesting that learning an effective representation for clustering is a crucial requirement. The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning. Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering. Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model. In this way, the multiple structures of data, from low-order to high-order, are naturally combined with the multiple representations learned by autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e., with the delivery operator, GCN improves the autoencoder-specific representation as a high-order graph regularization constraint and autoencoder helps alleviate the over-smoothing problem in GCN. Through comprehensive experiments, we demonstrate that our propose model can consistently perform better over the state-of-the-art techniques.

• The paper introduces a novel clustering model that fuses autoencoder and GCN representations using a unique delivery operator.
• The model applies a dual self-supervised mechanism to jointly optimize feature extraction and clustering, mitigating GCN over-smoothing.
• Experiments on six datasets demonstrate significant gains, with improvements of 17% NMI and 28% ARI over traditional methods.

Overview of "Structural Deep Clustering Network"

The paper "Structural Deep Clustering Network" introduces a novel approach to enhance deep clustering by integrating structural information derived from data itself. Traditional deep clustering methods primarily focus on improving clustering outputs through the robust representation capabilities of neural networks like autoencoders, typically neglecting the underlying data structure. The authors propose the Structural Deep Clustering Network (SDCN) to address this oversight by leveraging the structural insights captured via Graph Convolutional Networks (GCNs).

Key Contributions

1. Integration of Structural Information: The SDCN model fuses autoencoder-derived representations with those obtained from GCNs, which encapsulate data's graph-based structural information. The integration is achieved through a novel delivery operator that reconciles these different forms of representations.
2. Theoretical Advancements: The authors provide a theoretical analysis demonstrating that the delivery operator acts as a bridge, allowing GCN to impose a high-order graph regularization on the autoencoder-derived representations. This approach mitigates the over-smoothing problem often encountered in GCN layers.
3. Dual Self-Supervised Mechanism: To ensure cohesive representation and clustering, the model utilizes a dual self-supervised mechanism. This mechanism uses a target distribution to guide the joint optimization of autoencoder and GCN modules, aligning the clustering and classification tasks within a unified framework.

Experimental Results and Implications

The authors validate their approach through extensive experimentation on six datasets, which include image, record, text, and graph-type data. Notably, the SDCN achieves significant performance improvements over existing methods, recording substantial gains in metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and clustering accuracy. For instance, improvements of 17% in NMI and 28% in ARI were achieved on average over baseline methods.

These results highlight the potential of leveraging structural information in data for clustering tasks, which may open avenues for more sophisticated clustering solutions in complex, structured datasets. The integration of structural views with latent data representations could drive advancements in various domains, such as bioinformatics and social network analysis, where the relational structure is critical.

Future Directions

The work encourages further exploration into the intersection of deep learning and graph-based structural analysis, suggesting potential research directions involving more advanced graph neural networks or incorporating external knowledge graphs. Additionally, the dual self-supervised approach could be extended to involve more complex dependency modeling between the representations, ensuring enhanced adaptability and performance.

In summary, the Structural Deep Clustering Network offers a compelling paradigm for clustering endeavors by bridging representation learning and structured information extraction, providing an impactful shift in handling clustering tasks across various domains.