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Found 11 papers, 1 papers with code
Neuroscience needs Network Science
We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities.
Zoo Guide to Network Embedding
Networks have provided extremely successful models of data and complex systems.
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks.
Persistent Dirac for molecular representation
A series of physical persistent attributes, which characterize the spectrum of the Dirac matrices across a filtration, are proposed and used as efficient molecular fingerprints.
Dirac signal processing of higher-order topological signals
We discuss in detail the properties of the Dirac operator including its spectrum and the chirality of its eigenvectors and we adopt this operator to formulate Dirac signal processing that can filter noisy signals defined on nodes, links and triangles of simplicial complexes.
Local topological moves determine global diffusion properties of hyperbolic higher-order networks
In this perspective, the spectral dimension plays a key role since it determines the effective dimension for diffusion processes on a network.
Statistical Mechanics Disordered Systems and Neural Networks Physics and Society
Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks
The colocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network.
Physics and Society Adaptation and Self-Organizing Systems
Beyond COVID-19: Network science and sustainable exit strategies
On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc.
Physics and Society
The spectral dimension of simplicial complexes: a renormalization group theory
Simplicial complexes are increasingly used to study complex system structure and dynamics including diffusion, synchronization and epidemic spreading.
Classical Information Theory of Networks
Existing information-theoretic frameworks based on maximum entropy network ensembles are not able to explain the emergence of heterogeneity in complex networks.
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space
Complex network topologies and hyperbolic geometry seem specularly connected, and one of the most fascinating and challenging problems of recent complex network theory is to map a given network to its hyperbolic space.
