Multi-view Graph Convolutional Network with Fully Leveraging Consistency
Summary: arXiv:2603.26729v1 Announce Type: cross
In the rapidly evolving field of machine learning, the effective utilization of consistency is critical for improving the performance of multi-view learning systems. Graph Convolutional Networks (GCNs) are increasingly being adopted to leverage node connections, which facilitates the propagation of information across graphs. This approach, while promising, is not without its limitations. Recent studies highlight several challenges that hinder the efficacy of existing GCN-based multi-view methods.
Challenges in Existing GCN Methods
- Topology Construction Limitations: Most current methods rely heavily on K-Nearest Neighbors (KNN) for constructing graph topology. This reliance on a predetermined k value often constrains the effective exploitation of inter-node consistency.
- Neglected Inter-Feature Consistency: Existing models frequently overlook the inter-feature consistency within individual views. This oversight can significantly compromise the quality of the final embedding representations.
- Inadequate Inter-View Consistency Utilization: Many methods do not fully capitalize on inter-view consistency. The fusion of embedded representations from multiple views typically occurs only after the intra-view graph convolutional operation, limiting the model’s ability to capture comprehensive data relations.
Introducing MGCN-FLC
To address these inherent challenges, researchers have proposed a novel approach known as the Multi-View Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement, and Interactive Fusion (MGCN-FLC). This innovative model aims to fully leverage three key types of consistency through the implementation of three specialized modules:
- Topology Construction Module: Utilizing the granular ball algorithm, this module clusters nodes into granular balls characterized by high internal similarity. This design enhances the capture of inter-node consistency.
- Feature Enhancement Module: This module is dedicated to improving feature representations by effectively capturing inter-feature consistency, thus enriching the data representation.
- Interactive Fusion Module: This component enables each view to interact deeply with all other views. Such interaction fosters a more comprehensive understanding of inter-view consistency, thereby enhancing the overall learning capability of the model.
Experimental Validation
The performance of the proposed MGCN-FLC has been rigorously evaluated across nine different datasets. The experimental results demonstrate that MGCN-FLC consistently outperforms state-of-the-art semi-supervised node classification methods. This advancement not only underscores the potential of leveraging consistency in multi-view learning but also paves the way for future research aimed at optimizing GCN applications.
Conclusion
The introduction of MGCN-FLC represents a significant step forward in the field of multi-view learning. By addressing the critical limitations of existing GCN-based methods, this new approach offers a robust framework for effectively utilizing consistency, thereby enhancing the learning capabilities of machine learning models. As the demand for sophisticated data analysis continues to grow, innovations such as MGCN-FLC will be instrumental in driving progress in the field.