This paper aims to provide a comprehensive critical overview on how entities and their
interactions in Complex Networked Systems (CNS) are modelled across disciplines as they …

Semi-supervised learning on graphs is a widely applicable problem in network science and
machine learning. Two standard algorithms---label propagation and graph neural networks …

In this work, we aim to accurately predict the number of hospitalizations during the COVID-
19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising …

Social network structures have an additional effect on success other than individual factors.
Whether this is true for failure, the opposite of success, still remains unclear. This paper …

Social network-based information campaigns can be used for promoting beneficial health
behaviours and mitigating polarization (eg regarding climate change or vaccines). Network …

Recognizing matches aspects of the circumstances to previous situations. It is proposed that
an individual's mind uses such similarities between present and past as discrimination …

Social networks play a crucial role in providing valuable contextual information across
disciplines and applications. However, the unobservable nature of physical-world social …

In this paper, we introduce a long-term follow-up specific individual searching (SIS) model.
This model introduces the concept of node search contributions by considering the …

The industry is the key to economic development. This paper proposed a fractal evolutionary
dynamic model of industry based on the propagation dynamics theory from the perspective …

In a socially connected business, the churn of a user can lead to reduced consumption of
her peers, which can potentially spread to other users in the network. We investigate these …