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Machine learning meets complex networks via coalescent embedding in the hyperbolic space, , , , and . Nature Communications, 8 (1): 1615 (2017)Generative modelling and inverse problem solving for networks in hyperbolic space. University of Dresden, Germany, (2019)base-search.net (fttudresden:oai:qucosa:de:qucosa:34911).Rich-clubness test: how to determine whether a complex network has or doesn't have a rich-club?, and . CoRR, (2017)Geometrical congruence and efficient greedy navigability of complex networks., and . CoRR, (2020)Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?, , , , and . Appl. Netw. Sci., (2017)Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding., and . CoRR, (2019)Minimum curvilinear automata with similarity attachment for network embedding and link prediction in the hyperbolic space., and . CoRR, (2018)A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities., and . CoRR, (2017)Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks., and . CoRR, (2018)Nonlinear Markov Clustering by Minimum Curvilinear Sparse Similarity., , , , and . CoRR, (2019)