Graph convolutional networks (GCN) with one or two hidden layers have been widely used
in handling graph data that are prevalent in various disciplines. Many studies showed that …

Multi-View clustering has attracted broad attention due to its capacity to utilize consistent
and complementary information among views. Although tremendous progress has been …

Multi-view semi-supervised classification is a typical task to classify data using a small
amount of supervised information, which has attracted a lot of attention from researchers in …

Abstract Graph Convolutional Network (GCN) has drawn widespread attention in data
mining on graphs due to its outstanding performance and rigor theoretical guarantee …

Multi-view learning is a promising research field that aims to enhance learning performance
by integrating information from diverse data perspectives. Due to the increasing interest in …

This paper proposes a novel approach to semantic representation learning from multi-view
datasets, distinct from most existing methodologies which typically handle single-view data …

Abstract Graph Convolutional Network (GCN) has become a hotspot in graph-based
machine learning due to its powerful graph processing capability. Most of the existing GCN …

Graph convolutional network has been extensively employed in semi-supervised
classification tasks. Although some studies have attempted to leverage graph convolutional …

Existing representation learning approaches lie predominantly in designing models
empirically without rigorous mathematical guidelines, neglecting interpretation in terms of …

Sample labeling is the most primary and fundamental step of semi-supervised learning. In
literature, most existing methods randomly label samples with a given ratio, but achieve …