Contrastive Multi-View Graph Hashing

2025/08/17 by Xu Yang, Xu, Yang, Kai Ming Ting +2
Computer Science · #Advanced Image and Video Retrieval Techniques #Algorithms and Data Compression #FOS: Computer and information sciences #Graph Theory and Algorithms #Information Retrieval (cs.IR)

paper · pdf · doi:10.48550/arxiv.2508.12377

openalex publication_date 2025/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Multi-view graph data, which both captures node attributes and rich relational information from diverse sources, is becoming increasingly prevalent in various domains. The effective and efficient retrieval of such data is an important task. Although multi-view hashing techniques have offered a paradigm for fusing diverse information into compact binary codes, they typically assume attributes-based inputs per view. This makes them unsuitable for multi-view graph data, where effectively encoding and fusing complex topological information from multiple heterogeneous graph views to generate unified binary embeddings remains a significant challenge. In this work, we propose Contrastive Multi-view Graph Hashing (CMGHash), a novel end-to-end framework designed to learn unified and discriminative binary embeddings from multi-view graph data. CMGHash learns a consensus node representation space using a contrastive multi-view graph loss, which aims to pull k-nearest neighbors from all graphs closer while pushing away negative pairs, i.e., non-neighbor nodes. Moreover, we impose binarization constraints on this consensus space, enabling its conversion to a corresponding binary embedding space at minimal cost. Extensive experiments on several benchmark datasets demonstrate that CMGHash significantly outperforms existing approaches in terms of retrieval accuracy.

Citations

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Related

• Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing
• Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering
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• Graph-based Multi-view Binary Learning for Image Clustering
• Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions