SNEGAN: Signed Network Embedding by Using Generative Adversarial Nets

Abstract

Network embedding (NE) aims to learn low-dimensional node representations of networks while preserving essential node structures and properties. Existing NE methods mainly preserve simple link structures in unsigned networks, neglecting conflicting relationships that widely exist in social media and Internet of things. In this paper, we propose a novel generative adversarial nets learning framework (called SNEGAN) for signed network embedding, which tries to preserve link structures signed by positive or negative labels. SNEGAN combines a generator with a signed random walker technique and a graph softmax function to generate fake links that are used to deceive discriminator. Moreover, it combines a discriminator with a tanh function to discriminate truths and signs of links sampled from the generator and real network structures. The generator and discriminator play a two-player minimax game, and they will eventually generate low-dimensional node representations of signed networks. Extensive experiments on both LFR benchmark and real-world signed networks show the superiority of SNEGAN over the state-of-the-art NE methods in tackling both link (sign) prediction and reconstruction tasks.