Simple Unsupervised Graph Representation Learning

Citations Over TimeTop 10% of 2022 papers

Abstract

In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the inter-class variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in small generalization error, thereby obtaining an effective model. Furthermore, our method removes widely used data augmentation and discriminator from previous graph contrastive learning methods, meanwhile available to output low-dimensional embeddings, leading to an efficient model. Experimental results on various real-world datasets demonstrate the effectiveness and efficiency of our method, compared to state-of-the-art methods. The source codes are released at https://github.com/YujieMo/SUGRL.

Related Papers

• → A high-performance, low-cost, leading edge discriminator(2005)8 cited
• → Discriminator-Quality Evaluation GAN(2022)4 cited
• Design and Implementation of the Discriminator with Half Approximation Structure in Code Tracking Loop for GPS(2010)
• → Analysis and computer simulation of a delay discriminator(2004)1 cited
• → Dynamically Masked Discriminator for Generative Adversarial Networks(2023)1 cited