MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
This work addresses the challenge of effectively analyzing multi-layer graphs for tasks like node classification, which is important for domains with complex relational data, though it appears incremental as it adapts GCN to a multi-layer setting.
The authors tackled the problem of semi-supervised node classification in multi-layer graphs by proposing MGCN, a method that uses Graph Convolutional Networks to incorporate node attributes and both within- and between-layer edges, achieving superior performance over existing multi-layer and single-layer competitors.
Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges.