Virus propagation power of the dynamic network

Abstract

With the development of mobile networks, propagation characteristics and defense mechanism of the virus have attracted increasing research attention. But current researches are mainly concerned with static network topology or community structure and some studies focus on characteristics of virus and malware. Little attention is paid to the influence of dynamic changes of network topology to virus propagation. Meanwhile, many studies focus on the threshold rate of the infection (or the immunization rate) for virus outbreak. In this paper, we present a new way to assess and restrain virus propagation by proposing the concepts of propagation power and propagation structure. Three basic propagation structures are presented through which the infection risk can be quantified, and a framework is proposed to assess the impact of virus propagation in different network structures. The relationship between the speed of virus propagation and network structure is also explored. An algorithm is designed to assess the propagation power in a dynamic network with no need to redetect the community under the dynamic network which may significantly improve the efficiency of assessment in a large-scale dynamic network. This study offers a feasible approach for quantifying the risk of virus infection in the network community which is valuable for designing and optimizing the virus defense systems.

Vaccination allocation in large dynamic networks

“…The Tree Building phase has two main goals using a similar idea structure to a multitask learning model [8]. Nodes are entered sequentially and processed for each goal.…”

“…Complications arise when new nodes vary in their importance to the networks due to location of edges and varying probability to infection [8]. For example a node could be added to the network that has edges to nodes that were not connected before or that create a cycle that was not present before the edge was added.…”

COV19-Dijkstra: A COVID-19 Propagation Model Based on Dijkstra’s Algorithm

“…In the weeks leading up to the diagnosis, the disease spread widely in mainland China and other countries around the world, causing global panic and resulting in the first major coronavirus pandemic in a few months. However, long before SARS-CoV-2, there was SARS-CoV-1 (Giannis et al, 2020;Ru et al, 2020), responsible for the SARS outbreak in 2002-2004 and MERS-CoV from the Middle East and South Korea, responsible for the MERS outbreak in 2012 (Cai et al, 2013;Giannis et al, 2020;Liang, 2020). Unlike these two viruses, a problematic feature of SARS-CoV-2 is the existence of asymptomatic infections (Giannis et al, 2020).…”

“…The objective pursued in this study being to develop a suitable conceptual model that allows modeling the transmission process while considering the specific characteristics of COVID-19 in particular, which is different from some models that have been used to describe the spread of biological viruses or virus propagation in a network such as: Susceptible Infected Susceptible (SIS) model (Cai et al, 2013;Liang, 2020;Song and Hei, 2020), Susceptible-Infected-Remised (SIR) model (Cai et al, 2013;Chen et al, 2020;Sameni, 2020), the Susceptible Exposed Infectious Recovered (SEIR) model (Cai et al, 2013;Faranda and Alberti, 2020;Rǎdulescu et al, 2020) and the Susceptible Infectious Recovered Dead (SIDR) model (Cai et al, 2013). Thus, this modeling of the COVID-19 which has become a pandemic, will therefore lead to large-scale scenarios, involving a great computational complexity, which led us to use the Dijkstra algorithm, to reduce this great computational complexity.…”

A Community Structure‐Based Approach for Network Immunization

“…Thus, it is important to immunize nodes based on the community structure of a network without requiring externally provided community labels of nodes. Importance of exploiting the community structure without explicit calculation is also pointed out for quantifying the risk of virus protection in dynamic networks (Cai et al ).…”