Spreading predictability in complex networks

Abstract

Spreading dynamics analysis is an important and interesting topic since it has many applications such as rumor or disease controlling, viral marketing and information recommending. Many state-of-the-art researches focus on predicting infection scale or threshold. Few researchers pay attention to the predicting of infection nodes from a snapshot. With developing of precision marketing, recommending and, controlling, how to predict infection nodes precisely from snapshot becomes a key issue in spreading dynamics analysis. In this paper, a probability based prediction model is presented so as to estimate the infection nodes from a snapshot of spreading. Experimental results on synthetic and real networks demonstrate that the model proposed could predict the infection nodes precisely in the sense of probability.Researchers have gotten many achievements on macro level of spreading such as 7 phase transition of spreading [26] and basic reproduction number [27]. Up to now, 8 many researches focus on estimating of infection scale. The simplest one is mean-field 9 model, in which, the spreading coverage can be predicted by using differential 10 equations [24]. Besides mean-field model, some more realistic models such as pair 11 approximation [25] and permutation entropy [28] are considered to predict the 12infection scale or infectious disease outbreaks. The main difference between mean-field 13 and pair approximation is that the former(latter) approximates high-order moments in 14 term of first (second) order ones. In [28], the researchers studied the predictability of a 15

Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

“…They discovered that under a 50% diagnosis rate and no public health intervention, the actual number of cases will be significantly higher than the reported number, whereas under a 70% public health intervention, the load on the health system will decrease substantially. Zhao et al [16] used a probability prediction model to accurately predict the infection nodes from snapshots of spreading. Fountain-Jones et al [17] used machine learning to establish pathogen-risk models and compared the prediction results of different machine learning methods and explained the results by using game theory.…”

Scale-free and small-world properties in an incompatibility hierarchical network

“…Substituting equation ( 4 , it can be obtained that 2 t k tends to infinity with the increasing of t , so there is 0 c  → . This shows disease will be transmitted in the network [15,21,22] as long as the transmission rate  is greater than 0. The network has a strong robustness when it is exposed to random attack [24][25][26] .…”