Attributed network representation learning via improved graph attention with robust negative sampling

Abstract

Attributed network representation learning is to embed graphs in low dimensional vector space such that the embedded vectors follow the differences and similarities of the source graphs. To capture structural features and node attributes of attributed network, we propose a novel graph auto-encoder method which is stacked encoder-decoder layers based on graph attention with robust negative sampling. Here, minimize the negative log-likelihood, triplet distance, and weighted neighborhood attributes are proposed as the loss function. To alleviate the over-fitting on reconstruct graph structural features or node attributes, a trade off algorithm between reconstruction loss of node attributes and reconstruction loss of structural features is proposed. Furthermore, to alleviate the impact of random sampling, we propose additional constraints on negative sampling based on node degree. Experimental results on several benchmark datasets for transductive and inductive learning tasks show that the proposed model is competitive against well-known methods in node classification and link prediction.