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Node classification, as a central task in the graph data analysis, has been studied extensively with network embedding technique for single-layer graph network. However, there are some obstacles when extending the single-layer network embedding technique to the attributed multiplex network. The classification of a given node in the attributed multiplex network must consider the network structure in different dimensions, as well as rich node attributes, and correlations among the different dimensions. Moreover, the distance node context information of a given node in each dimension will also affect the classification of the given node. In this study, a novel network embedding approach for the node classification of attributed multiplex networks using random walk and graph convolutional networks (AMRG) is proposed. A random walk network embedding technique was used to extract distant node information and the results are considered as pre-trained node features to be concatenated with the original node features inputted into the graph convolutional networks (GCNs) to learn node representations for each dimension. Besides, the consensus regularization is introduced to capture the similarities among different dimensions, and the learnable neural network parameters of GCNs for different dimensions are also constrained by the regularization mechanism to improve the correlations. As well as an attention mechanism is explored to infer the importance for a given node in different dimensions. Extensive experiments demonstrated that our proposed technique outperforms many competitive baselines on several real-world multiplex network datasets.

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges. First, most of the real‐word system are multiple relations, where entities are linked by different types of relations, and each relation is a view of the graph network. Second, the rich multi‐scale information (structure‐level and feature‐level) of the graph network can be seen as self‐supervised signals, which are not fully exploited. A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale (named CoLM2S) information is presented in this study. It mainly contains two components: intra‐relation contrast learning and inter‐relation contrastive learning. Specifically, the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information (CoLMS) framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level self‐supervised signals is introduced first. The structure‐level information includes the edge structure and sub‐graph structure, and the feature‐level information represents the output of different graph convolutional layer. Second, according to the consensus assumption among inter‐relations, the CoLM2S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding. The proposed method can fully distil the graph information. Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods, and it outperforms existing competitive baselines.

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