Density-Adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification

Abstract

Traditional network representation learning techniques aim to learn latent low-dimensional representation of vertices in graphs. This paper presents a novel edge representation learning framework, GANDLERL, that combines generative adversarial network based multi-label classification with density-adaptive local edge representation learning for producing high-quality low-dimensional edge representations. First, we design a generative adversarial network based multi-label edge classification model to classify rarely labeled edges in graphs with a large amount of noise data into K classes. A four-player zero-sum game model, with the mixed training of true and real-looking fake edges as well as a contrastive loss containing a similar-loss and a dissimilar-loss, is proposed to improve the classification quality of unlabeled edges. Second, a local autoencoder edge representation learning method is developed to design K local representation learning models, each with individual parameters and structure to perform local representation learning on each of K classification-based subgraphs with unique local characteristics and jointly optimize the loss functions within and across classes. Third but last, we propose a density-adaptive edge representation learning method with the optimization at both edge and subgraph levels to address the representation learning of graph data with highly imbalanced vertex degree and edge distribution.