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Conditional Noise-Contrastive Estimation of Unnormalised Models
Examples of unnormalised models are Gibbs distributions, Markov random fields, and neural network models in unsupervised deep learning.
GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs
We argue that when comparing two graphs, the distribution of node structural features is more informative than global graph statistics which are often used in practice, especially to evaluate graph generative models.
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs
Structural node embeddings, vectors capturing local connectivity information for each node in a graph, have many applications in data mining and machine learning, e. g., network alignment and node classification, clustering and anomaly detection.
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