no code implementations • 20 Jul 2022 • Ciwan Ceylan, Kambiz Ghoorchian, Danica Kragic
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.
no code implementations • 8 Feb 2022 • Ciwan Ceylan, Petra Poklukar, Hanna Hultin, Alexander Kravchenko, Anastasia Varava, Danica Kragic
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.
no code implementations • ICML 2018 • Ciwan Ceylan, Michael U. Gutmann
Examples of unnormalised models are Gibbs distributions, Markov random fields, and neural network models in unsupervised deep learning.