Social physics
Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …
phenomena. This development has been due to physicists venturing outside of their …
[HTML][HTML] Networks beyond pairwise interactions: Structure and dynamics
The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …
of the interactions among their units. Over the past decades, a variety of complex systems …
Signal propagation in complex networks
Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …
going viral, promotes trust and facilitates moral behavior in social groups, enables the …
More is different in real-world multilayer networks
M De Domenico - Nature Physics, 2023 - nature.com
The constituents of many complex systems are characterized by non-trivial connectivity
patterns and dynamical processes that are well captured by network models. However, most …
patterns and dynamical processes that are well captured by network models. However, most …
Graph neural networks with convolutional arma filters
Popular graph neural networks implement convolution operations on graphs based on
polynomial spectral filters. In this paper, we propose a novel graph convolutional layer …
polynomial spectral filters. In this paper, we propose a novel graph convolutional layer …
[HTML][HTML] Random walks and diffusion on networks
Random walks are ubiquitous in the sciences, and they are interesting from both theoretical
and practical perspectives. They are one of the most fundamental types of stochastic …
and practical perspectives. They are one of the most fundamental types of stochastic …
Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models
We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …
Adaptive dynamical networks
It is a fundamental challenge to understand how the function of a network is related to its
structural organization. Adaptive dynamical networks represent a broad class of systems that …
structural organization. Adaptive dynamical networks represent a broad class of systems that …
Temporally evolving graph neural network for fake news detection
The proliferation of fake news on social media has the probability to bring an unfavorable
impact on public opinion and social development. Many efforts have been paid to develop …
impact on public opinion and social development. Many efforts have been paid to develop …
Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …
recommender systems and epidemiology. Representing complex networks as structures …