Beyond local aggregation: Global graph contrastive learning for multi-view fusion

Multi-view fusion has emerged as an effective paradigm for learning unified embeddings from heterogeneous sources. Graph-based methods have gained recognition for their capability to capture and align cross-view structural relationships among instances. Currently, in the unsupervised setting, graph neural network-based multi-view learning faces three key challenges: constructing reliable graph topologies, aligning inter-view relationships, and exploiting structural information. In this paper, we propose the Global Graph Contrastive learning for Multi-view fusion (G²CM) algorithm to address these issues. To construct reliable graph topologies, we integrate a global topology with view-specific weighted edges, where the global topology encodes relational context and the view-specific weighting preserves local semantics within each view. These weighted adjacency matrices are then used to build individual graph convolutional networks for each view, allowing more precise modeling and integration of global and local structural information during representation learning. To enhance cross-view alignment, we introduce a contrastive learning framework with three types of positive pairs and two types of negative pairs to capture multi-level semantic relationships across views. By jointly modeling intra-view and inter-view alignments, the framework enhances the discriminative capability and semantic consistency of the learned embeddings. To better exploit structural information, we incorporate distance-aware scaling into the loss function, weighting sample pairs by their feature-space proximity. This mechanism enhances the influence of semantically relevant neighbors during optimization, improving the preservation of local proximity and utilization of structural information. Experiments on six benchmark multi-view datasets demonstrate that G²CM achieves state-of-the-art performance across diverse data types.