Contrastive Multi-View Graph Hashing

• URL: http://arxiv.org/abs/2508.12377v1
• Date: Sun, 17 Aug 2025 14:27:20 GMT
• Title: Contrastive Multi-View Graph Hashing
• Authors: Yang Xu, Zuliang Yang, Kai Ming Ting,
• Abstract summary: Contrastive Multi-view Graph Hashing (CMGHash) is a novel end-to-end framework designed to learn unified and discriminative binary embeddings from multi-view graph data.<n>Experiments on several benchmark datasets demonstrate that CMGHash significantly outperforms existing approaches in terms of retrieval accuracy.
• Score: 8.30973300514715
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training [31.639971171132615]
  Universal graph pre-training offers a promising way to train encoders to learn transferable representations from unlabeled graphs.<n>It remains unexplored for heterogeneous graphs, which exhibit greater structural and semantic complexity.<n>We propose a novel Meta-path-aware Universal heterogeneous Graph pre-training (MUG) approach.
  arXiv  Detail & Related papers  (2026-02-26T05:52:28Z)
• Co-Hub Node Based Multiview Graph Learning with Theoretical Guarantees [12.812858849900877]
  We propose a co-hub node model, positing that different views share a common group of hub nodes.<n>The proposed methodology is validated using both synthetic graph data and fMRI time series data from multiple subjects.
  arXiv  Detail & Related papers  (2025-12-13T19:25:35Z)
• Global Graph Propagation with Hierarchical Information Transfer for Incomplete Contrastive Multi-view Clustering [12.080981190754986]
  We propose a novel incomplete multi-view clustering method with hierarchical information transfer.<n>Our method combines view-specific representation learning, global graph propagation with hierarchical information transfer, and contrastive clustering for joint optimization.
  arXiv  Detail & Related papers  (2025-02-26T16:50:08Z)
• GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems [51.64666652517944]
  This paper introduces GraphHash, the first graph-based approach that leverages modularity-based bipartite graph clustering to reduce embedding table sizes.<n>By employing fast clustering algorithms, GraphHash serves as a computationally efficient proxy for message-passing during preprocessing.<n>In experiments, GraphHash substantially outperforms diverse hashing baselines on both retrieval and click-through-rate prediction tasks.
  arXiv  Detail & Related papers  (2024-12-23T03:37:58Z)
• Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation [84.45144851024257]
  We propose a novel framework that aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes.<n>The core idea is to map users and items into discrete codes rich in collaborative information for reliable and informative contrastive view generation.
  arXiv  Detail & Related papers  (2024-09-09T14:04:17Z)
• Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering [11.082316688429641]
  We propose a hashing algorithm based on auto-encoders for multi-view binary clustering. Specifically, we propose a multi-view affinity graphs learning model with low-rank constraint, which can mine the underlying geometric information from multi-view data. We also design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively.
  arXiv  Detail & Related papers  (2023-01-06T12:43:13Z)
• Multi-view Graph Convolutional Networks with Differentiable Node Selection [29.575611350389444]
  We propose a framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS) MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-12-09T21:48:36Z)
• Variational Graph Generator for Multi-View Graph Clustering [51.89092260088973]
  We propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC)<n>This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs.<n>It embeds the inferred view-common graph and view-specific graphs together with features.
  arXiv  Detail & Related papers  (2022-10-13T13:19:51Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Consistent Multiple Graph Embedding for Multi-View Clustering [41.17336912278538]
  We propose a novel Consistent Multiple Graph Embedding Clustering framework(CMGEC) Specifically, a multiple graph auto-encoder is designed to flexibly encode the complementary information of multi-view data. To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module(MMIM) is introduced.
  arXiv  Detail & Related papers  (2021-05-11T09:08:22Z)
• Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification [110.52328716130022]
  Video-based person re-identification (re-ID) is an important research topic in computer vision. We propose a novel graph-based framework, namely Multi-Granular Hypergraph (MGH) to better representational capabilities. 90.0% top-1 accuracy on MARS is achieved using MGH, outperforming the state-of-the-arts schemes.
  arXiv  Detail & Related papers  (2021-04-30T11:20:02Z)
• Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency [65.76554214664101]
  Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. We propose a new multi-view graph learning framework, which for the first time simultaneously models multi-view consistency and multi-view inconsistency in a unified objective function. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach.
  arXiv  Detail & Related papers  (2020-08-24T06:11:29Z)
• Auto-Encoding Twin-Bottleneck Hashing [141.5378966676885]
  This paper proposes an efficient and adaptive code-driven graph. It is updated by decoding in the context of an auto-encoder. Experiments on benchmarked datasets clearly show the superiority of our framework over the state-of-the-art hashing methods.
  arXiv  Detail & Related papers  (2020-02-27T05:58:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.