Contrastive Multi-View Graph Hashing
Revolutionizing Multi-View Graph Retrieval with CMGHash
This research introduces Contrastive Multi-view Graph Hashing (CMGHash), an innovative end-to-end framework for learning unified and discriminative binary embeddings from complex multi-view graph data. Unlike prior methods, CMGHash effectively encodes and fuses intricate topological information from multiple heterogeneous graph views. It achieves this by using a contrastive multi-view graph loss to bring k-nearest neighbors closer and push negative pairs apart in a consensus node representation space. Binarization constraints are then applied to convert this space into compact binary embeddings with minimal information loss. Extensive experiments across several benchmark datasets demonstrate CMGHash's significant superiority in retrieval accuracy compared to existing approaches, marking it as the first framework to generate binary codes from multi-view graph topologies.
Key Executive Impact
CMGHash significantly advances enterprise data retrieval by providing unparalleled efficiency and accuracy for complex, multi-view graph data.
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Novel Framework Developed
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Benchmark Datasets Tested
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Peak mAP@all (ACM 32-bit)
Deep Analysis & Enterprise Applications
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Problem
Solution
Impact
Existing multi-view hashing techniques are unsuitable for multi-view graph data because they assume attribute-based inputs per view, failing to encode and fuse complex topological information from heterogeneous graph views. The challenge is to generate unified, discriminative binary embeddings efficiently.
CMGHash is an end-to-end framework that learns unified and discriminative binary embeddings from multi-view graph data. It uses graph filtering for smoothed representations and a kNN-based contrastive multi-view graph loss to align positive pairs across views while separating negative ones. Binarization constraints are applied to convert continuous representations into compact binary codes with minimal loss.
CMGHash significantly outperforms existing approaches in retrieval accuracy across five benchmark datasets. It provides a foundational solution for efficient and effective retrieval of multi-view graph data, opening new avenues for research in large-scale graph applications and downstream tasks.
84.4%
Peak mAP@all score on ACM dataset (32 bits), demonstrating superior retrieval accuracy.
CMGHash End-to-End Workflow
Multi-view Graph Data Input (V,X,E)
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Graph Filtering (Smoothed Features S(v))
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kNN-based Multi-view Graph Contrastive Learning
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Binarization Losses (LQ + LBB)
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Unified Continuous Graph Representations (U)
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Binary Embeddings (B)
| Feature | CMGHash | Traditional Multi-view Hashing |
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| Performance (Retrieval Accuracy) |
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| Handling of Complex Topologies |
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Ablation Study: Impact of CMGHash Components
An ablation study reveals the critical contribution of each component to CMGHash's superior performance. The 'CMGHash-f' variant (without graph filtering) shows a remarkable drop in mAP@all scores, highlighting the necessity of denoising and smoothing features. 'CMGHash-q' (without quantization loss) and 'CMGHash-b' (without bit balance loss) also exhibit slight but noticeable drops, confirming their roles in preserving information and guiding the model to learn discriminative binary codes. This reinforces that all integrated components are vital for optimal retrieval accuracy.
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Collection, cleaning, and preparation of relevant data. Development and training of custom AI models, leveraging state-of-the-art techniques like CMGHash for graph data.
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