Indexed by:
Abstract:
As a promising area in machine learning, multi-view learning enhances model performance by integrating data from various views. With the rise of graph convolutional networks, many studies have explored incorporating them into multi-view learning frameworks. However, these methods often require storing the entire graph topology, leading to significant memory demands. Additionally, iterative update operations in graph convolutions lead to longer inference times, making it difficult to deploy existing multi-view learning models on large graphs. To overcome these challenges, we introduce an efficient multi-view graph convolutional network via local aggregation and global propagation. In the local aggregation module, we use a structure-aware matrix for feature aggregation, which significantly reduces computational complexity compared to traditional graph convolutions. After that, we design a global propagation module that allows the model to be trained in batches, enabling deployment on large-scale graphs. Finally, we introduce the attention mechanism into multi-view feature fusion to more effectively explore the consistency and complementarity between views. The proposed method is employed to perform multi-view semi-supervised classification, and comprehensive experimental results on benchmark datasets validate its effectiveness. © 2024 Elsevier Ltd
Keyword:
Reprint 's Address:
Email:
Source :
Expert Systems with Applications
ISSN: 0957-4174
Year: 2025
Volume: 266
7 . 5 0 0
JCR@2023
Cited Count:
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
Affiliated Colleges: