Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion
📰 ArXiv cs.AI
Multi-view Graph Convolutional Network leverages consistency via granular-ball-based topology construction and interactive fusion for effective multi-view learning
Action Steps
- Construct topology using granular-ball-based method to capture complex relationships
- Enhance features through node connections and information propagation
- Fusion of multiple views via interactive methods to fully leverage consistency
- Evaluate the effectiveness of the proposed method on various multi-view learning tasks
Who Needs to Know This
AI engineers and researchers on a team can benefit from this approach to improve multi-view learning, and data scientists can apply this method to various applications
Key Insight
💡 Granular-ball-based topology construction and interactive fusion can effectively leverage consistency in multi-view learning
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🤖 Multi-view Graph Convolutional Network boosts consistency via granular-ball-based topology & interactive fusion!
Key Takeaways
Multi-view Graph Convolutional Network leverages consistency via granular-ball-based topology construction and interactive fusion for effective multi-view learning
Full Article
Title: Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion
Abstract:
arXiv:2603.26729v1 Announce Type: cross Abstract: The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation of consistency in multi-view data. However, most existing GCN-based multi-view methods suffer from several limitations. First, current approaches predominantly rely on KNN for topology construction, where the artificial selection of the k value significantly constrains
Abstract:
arXiv:2603.26729v1 Announce Type: cross Abstract: The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation of consistency in multi-view data. However, most existing GCN-based multi-view methods suffer from several limitations. First, current approaches predominantly rely on KNN for topology construction, where the artificial selection of the k value significantly constrains
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