Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification

Abstract

Complex networks are used as an abstraction for systems modeling in physics, biology, sociology, and other areas. We propose an algorithm, named Deep Node Ranking (DNR), based on fast personalized node ranking and the approximation power of deep learning for learning supervised and unsupervised network embeddings, as well as for classifying network nodes directly. The experiments demonstrate that the DNR algorithm is competitive with strong baselines on nine node classification benchmarks from the domains of molecular biology, finance, social media and language processing in terms of speed, as well as predictive accuracy. Embeddings, obtained by the proposed algorithm, are also a viable option for network visualization.