Artificial intelligence is an advanced method to identify novel anticancer targets and discover
novel drugs from biology networks because the networks can effectively preserve and …

The propagations of diseases, behaviors and information in real systems are rarely
independent of each other, but they are coevolving with strong interactions. To uncover the …

Abstract Models of epidemic spreading on complex networks have attracted great attention
among researchers in physics, mathematics, and epidemiology due to their success in …

Numerous centrality measures have been developed to quantify the importances of nodes in
time-independent networks, and many of them can be expressed as the leading eigenvector …

The Nature of Complex Networks provides a systematic introduction to the statistical
mechanics of complex networks and the different theoretical achievements in the field that …

Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major
step to better describe complex systems. In the resulting hypergraph representation, tools to …

Time-dependent density functional theory (TD-DFT) is the most widely-used electronic
structure method for excited states, due to a favorable combination of low cost and semi …

Discrete-and continuous-time approaches are frequently used to model the role of
heterogeneity on dynamical interacting agents on the top of complex networks. While, on the …

Graph theory is now becoming a standard tool in system-level neuroscience. However,
endowing observed brain anatomy and dynamics with a complex network structure does not …

We present a simple neural network that can learn modular arithmetic tasks and exhibits a
sudden jump in generalization known as``grokking''. Concretely, we present (i) fully …