Leveraging network structure for efficient dynamic negative sampling in network embedding

Unsupervised network embedding learns low-dimensional vector representations of nodes based on the network structure. However, typical graphs only contain positive edges. Hence, most network embedding models take sampled negative edges as input to avoid trivial solutions. However, conventional negative sampling methods follow fixed distributions to generate negative samples. Such a static sampling strategy could dramatically hurt the performance of node embeddings in downstream tasks. Researchers have proposed several adaptive sampling strategies to improve the quality of negative samples. However, existing methods do not sufficiently use the network structure to explore high-quality samples, resulting in complex and inefficient negative sampling. This paper develops a novel dynamic negative sampling scheme, which maintains a candidate sample population for each node. We update the populations dynamically and retain high-quality samples in each iteration. We develop a novel network embedding algorithm based on the proposed model, which selects high-quality negative samples adaptively from the population. We conduct extensive experiments to evaluate its effectiveness based on several benchmark datasets. The experimental results verify the superiority of the proposed methods over state-of-the-art approaches.(c) 2022 Elsevier Inc. All rights reserved.