Node Pair Information Preserving Network Embedding Based on Adversarial Networks.

Abstract

Network embedding aims to learn the low-dimensional node representations for networks, which has attracted an increasing amount of attention in recent years. Most existing efforts in this field attempt to embed the network based on node similarity, which generally relies on edge existence statistics of the network. Instead of relying on the global edge existence statistics for every node pair, in this article, we utilize the information between a pair of nodes in a local way and propose a model, called node pair information preserving network embedding (NINE), based on adversarial networks. The main idea lies in preserving the node pair information (NI) by means of adversarial networks. The architecture of the proposed NINE model consists of three main components, namely: 1) NI embedder; 2) NI generator; and 3) NI discriminator. In the NI embedder, to avoid the complicated similarity calculation for a pair of nodes, the original NI vector calculated from the direct neighbor information of the two nodes is adopted as features, and the edge existence information is taken as labels to learn the embedded NI vector in a supervised learning manner. The second component is the NI generator, which takes the original node representation vectors of a node pair as input and outputs the generated NI vector. In order to make the generated NI vector follow the same distribution of the corresponding embedded NI vector, the generative adversarial network (GAN) is adopted, resulting in the third component, called the NI discriminator. Extensive experiments are conducted on seven real-world datasets in three downstream tasks, namely: 1) network reconstruction; 2) link prediction; and 3) node classification. Comparison results with seven state-of-the-art models demonstrate the effectiveness, efficiency, and rationality of our model.