.png){kind=icon}
.png){kind=icon}
Return
MBGCN: Multi-View Block-Wise Graph Convolutional Networks on Large-Scale Graphs
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2025.3640824.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Existing methods based on graph convolutional network often struggle with large-scale graphs due to their high computing consumption and inefficiency. Although strategies such as edge sparsification and node sampling can indeed decrease the complexity, they frequently result in information loss and local information bias. Furthermore, in multi-view scenarios, traditional multi-view fusion methods are unable to simultaneously account for both inter-view consistency and intra-view diversity, thus constraining model performance. In this paper, we propose a multi-view block-wise graph convolutional network that effectively addresses the challenges posed by large-scale graphs while exploiting the complementary nature of multi-view information. Specifically, we implement a node segmentation module to partition nodes into view-specific subsets, thereby diminishing computational complexity while preserving local structural information. To enhance feature extraction, plentiful subgraph representations are captured within blocks by alternating graph convolution with graph structure learning under a shared-weight strategy. Finally, the global fusion module introduces a cross-view inter-block loss that progressively aligns block representations across views, alleviates over-smoothing, and yields a consistent and comprehensive common representation. Extensive experiments on diverse large-scale graph datasets demonstrate that the proposed method not only outperforms state-of-the-art approaches in multi-view semi-supervised classification but also exhibits superior scalability and memory efficiency.
Keywords:
Multi-view learning
graph convolutional network
graph structure learning
semi-supervised classification
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W