.png){kind=icon}
.png){kind=icon}
Return
Robustness Controlled Adversarial Graph Convolutional Network for Multi-view Semi-supervised Classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2026.115789.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-view learning based on graph convolutional networks can capitalize on supervisory information from heterogeneous data views to improve performance, thereby attracting increasing attention in various practical fields. Although existing research and applications have made significant progress, they mostly consider feature and topological structure learning separately, resulting in underutilization of learned representations or topological structures and susceptibility to noise. To address these challenges, we first propose a bi-level optimization framework for an interpretable graph neural network in an end-to-end manner, then introduce adversarial training into multi-view learning, modeling the multi-view adversarial graph convolutional problem as a minimax optimization problem. Specifically, the lower-level optimization maximizes graph regularization within a robust control range to refine the graph Laplacian, while the upper-level minimizes the loss caused by the lower-level perturbations to enhance representation robustness. This jointly improves model adaptability to noisy data. The proposed approach aims to synchronize representation learning and topological adjustment to ensure effective information transfer between features and topological structures. It represents an efficient solution for multi-view graph convolutional networks within the dynamic graph structure learning paradigm. Extensive experiments validate our method’s effectiveness across diverse real-world multi-view datasets.
Keywords:
Multi-view learning
Graph convolutional networks
Adversarial training
Robustness control
Semi-supervised classification
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
Organization
View more organizations