Unsupervised Attributed Multiplex Network Embedding

Understand

Nodes in a multiplex network are connected by multiple types of relations.

• However, most existing network embedding methods assume that only a single type of relation exists between nodes.
• Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph.
• We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph.