1 research outputs found
Deep Node Ranking: Structural Network Embedding and End-to-End Node Classification
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 raw 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