Social physics

M Jusup, P Holme, K Kanazawa, M Takayasu, I Romić… - Physics Reports, 2022 - Elsevier
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 …

[HTML][HTML] Networks beyond pairwise interactions: Structure and dynamics

F Battiston, G Cencetti, I Iacopini, V Latora, M Lucas… - Physics reports, 2020 - Elsevier
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 …

Signal propagation in complex networks

P Ji, J Ye, Y Mu, W Lin, Y Tian, C Hens, M Perc, Y Tang… - Physics reports, 2023 - Elsevier
Signal propagation in complex networks drives epidemics, is responsible for information
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 …

Graph neural networks with convolutional arma filters

FM Bianchi, D Grattarola, L Livi… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Popular graph neural networks implement convolution operations on graphs based on
polynomial spectral filters. In this paper, we propose a novel graph convolutional layer …

[HTML][HTML] Random walks and diffusion on networks

N Masuda, MA Porter, R Lambiotte - Physics reports, 2017 - Elsevier
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 …

Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models

B Rozemberczki, P Scherer, Y He… - Proceedings of the 30th …, 2021 - dl.acm.org
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 …

Adaptive dynamical networks

R Berner, T Gross, C Kuehn, J Kurths, S Yanchuk - Physics Reports, 2023 - Elsevier
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 …

Temporally evolving graph neural network for fake news detection

C Song, K Shu, B Wu - Information Processing & Management, 2021 - Elsevier
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 …

Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey

J Skarding, B Gabrys, K Musial - iEEE Access, 2021 - ieeexplore.ieee.org
Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …