Zobrazeno 1 - 10
of 278
pro vyhledávání: '"Boguñá, Marián"'
Graph Neural Networks (GNNs) have excelled in predicting graph properties in various applications ranging from identifying trends in social networks to drug discovery and malware detection. With the abundance of new architectures and increased comple
Externí odkaz:
http://arxiv.org/abs/2406.02772
The Renormalization Group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their topology is based on the location of nodes in a hidden metric space, is
Externí odkaz:
http://arxiv.org/abs/2403.12663
Autor:
Boguna, Marian, Krioukov, Dmitri
Causal set theory is perhaps the most minimalistic approach to quantum gravity, in the sense that it makes next to zero assumptions about the structure of spacetime below the Planck scale. Yet even with this minimalism, the continuum limit is still a
Externí odkaz:
http://arxiv.org/abs/2401.17376
In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces $FiD$-Mercator, a model-based ultra-low dimensional
Externí odkaz:
http://arxiv.org/abs/2401.09368
Graph-structured data provide a comprehensive description of complex systems, encompassing not only the interactions among nodes but also the intrinsic features that characterize these nodes. These features play a fundamental role in the formation of
Externí odkaz:
http://arxiv.org/abs/2307.14198
The geometric renormalization technique for complex networks has successfully revealed the multiscale self-similarity of real network topologies and can be applied to generate replicas at different length scales. In this letter, we extend the geometr
Externí odkaz:
http://arxiv.org/abs/2307.00879
One of the pillars of the geometric approach to networks has been the development of model-based mapping tools that embed real networks in its latent geometry. In particular, the tool Mercator embeds networks into the hyperbolic plane. However, some
Externí odkaz:
http://arxiv.org/abs/2304.06580
Many empirical studies have revealed that the occurrences of contacts associated with human activities are non-Markovian temporal processes with a heavy tailed inter-event time distribution. Besides, there has been increasing empirical evidence that
Externí odkaz:
http://arxiv.org/abs/2303.04740
First principle network models are crucial to make sense of the intricate topology of real complex networks. While modeling efforts have been quite successful in undirected networks, generative models for networks with asymmetric interactions are sti
Externí odkaz:
http://arxiv.org/abs/2302.09055
Autor:
van der Kolk, Jasper, García-Pérez, Guillermo, Kouvaris, Nikos E., Serrano, M. Ángeles, Boguñá, Marián
Turing patterns, arising from the interplay between competing species of diffusive particles, has long been an important concept for describing non-equilibrium self-organization in nature, and has been extensively investigated in many chemical and bi
Externí odkaz:
http://arxiv.org/abs/2211.11311