Network embedding aims to learn low-dimensional representations of nodes in a network, which can be used for many downstream network analysis tasks. Recently, many network embedding methods based on Generative Adversarial Networks (GANs) have been proposed. However, GAN-based methods mainly face two challenges: (1) Existing GAN-based methods often use GANs to learn Gaussian distributions as … Read more
Background Introduction The main purpose of Graph Representation Learning (Graph Representation Learning, Network Embedding, Graph Embedding) is to map each node in the graph to a low-dimensional vector space, preserving the original structure and distance information of the graph. Intuitively, two points that are close together in the graph should also be close together in … Read more
This article is a transcript of the live sharing session by Wang Hongwei, a PhD student from Shanghai Jiao Tong University and intern at Microsoft Research Asia, on January 10 during the 23rd PhD Talk. Network representation learning (network embedding) has emerged in recent years as a branch of feature learning research. As a dimensionality … Read more