MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks

In many machine learning applications, data are coming with multiple graphs, which is known as the multiple graph learning problem. The problem of multiple graph learning is to learn consistent representation by exploiting the complementary information of multiple graphs. Graph Learning Neural Networks (GLNNs) have been demonstrated powerfully for graph data representation and semi supervised classification tasks. However, Existing GLNNs are mainly developed for single graph data which cannot be utilized for multiple graph data representation. In this paper, we propose a novel learning framework, called Multiple Graph Learning Neural Networks (MGLNN), for multiple graph learning and multi-view semi-supervised classification. The goal of MGLNN is to learn an optimal graph structure from multiple graph structures that best serves GNNs' learning which integrates multiple graph learning and GNNs' representation simultaneously. The proposed MGLNN is a general framework which can incorporate any specific GNN model to deal with multiple graphs. A general algorithm has also been developed to optimize/train the proposed MGLNN model. Experimental results on several datasets demonstrate that MGLNN outperforms some other related methods on semi-supervised classification tasks. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

The problem of multiple graph learning involves learning consistent representation by exploiting the complementary information of multiple graphs. This paper proposes a novel learning framework, called Multiple Graph Learning Neural Networks (MGLNN), which aims to learn an optimal graph structure from multiple graph structures and integrate multiple graph learning and Graph Neural Networks' representation. Experimental results demonstrate that MGLNN outperforms other methods on semi-supervised classification tasks.