Identify information sources with different start times in complex networks based on sparse observers

The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial moments.Although there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains unresolved.In this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start times.The proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread duration.The core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear programming.Extensive experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and efficiency.Furthermore,we find that our method maintains robustness irrespective of the number of sources and the average degree of network.Compared with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.

Topological phase transition in network spreading

This paper investigates information spreading from the perspective of topological phase transition.Firstly,a new hybrid network is constructed based on the small-world networks and scale-free networks.Secondly,the attention mechanism of online users in information spreading is studied from four aspects:social distance,individual influence,content richness,and individual activity,and a dynamic evolution model of connecting with spreading is designed.Eventually,numerical simulations are conducted in three types of networks to verify the validity of the proposed dynamic evolution model.The simulation results show that topological structure and node influence in different networks have undergone phase transition,which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short period.The infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution rule.Furthermore,the simulation results are compared with the real data,which shows that the infection density curve of the hybrid networks is closer to that of the real data than that of the small-world networks and scale-free networks,verifying the validity of the model proposed in this paper.

Shortest path of temporal networks:An information spreading-based approach

The shortest path is a widely studied network science problem and has attracted great attention.Nevertheless,it draws little attention in temporal networks,in which temporal edges determine information dissemination.In this paper,we propose an information spreading-based method to calculate the shortest paths distribution in temporal networks.We verify our method on both artificial and real-world temporal networks and obtain a good agreement.We further generalize our method to identify influential nodes and found an effective method.Finally,we verify the influential nodes identifying method on four networks.

Competition between two kinds of information among random-walking individuals

A model is proposed to describe the competition between two kinds of information among N random-walking individuals in an L x L square, starting from a half-and-half mixture of two kinds of information. Individuals remain or change their information according to their neighbors＇ information. When the moving speed of individuals v is zero, the two kinds of information typically coexist, and the ratio between them increases with L and decreases with N. In the dynamic case （v 〉 0）, only one information eventually remains, and the time required for one information being left scales as Td -v^αL^β^γ.

Information overload:How hot topics distract from news——COVID-19 spread in the US

Information dissemination and the associated change of individual behavior can significantly slow the spread of an epidemic.However,major social events which attract public attention will disturb information spread and affect epidemic transmission in ways that have not been readily quantified.We investigate the interplay between disease spreading and diseaserelated information dissemination in a two-layer network.We employ the SIR-UAU model with a time dependent coefficient to denote information dissemination.We found that major social events are equivalent to perturbations of information dissemination in certain time intervals and will consequently weaken the effect of information dissemination,and increase prevalence of infection.Our simulation results agree well with the trends observed from real-world data sets.We found that two specific major events explain the trend of the coronavirus epidemic in the US:the online propaganda and international agenda setting of Donald Trump early in 2020 and the 2020 US Presidential Election.

Out-of-time-order correlators in the one-dimensional XY model

We study the behavior of information spreading in the XY model, using out-of-time-order correlators(OTOCs). The effects of anisotropic parameter γ and external magnetic field λon OTOCs are studied in detail within thermodynamical limits. The universal form which characterizes the wavefront of information spreading still holds in the XY model. The butterfly speed vBdepends on(γ, λ). At a fixed location, the early-time evolution behavior of OTOCs agrees with the results of the Hausdorff–Baker–Campbell expansion. For long-time evolution,OTOCs with local operators decay as for power law t^-1, but those with nonlocal operators show different and nontrivial power law behaviors. We also observe temperature dependence for OTOCs when(γ=0, λ=1). At low temperature, the OTOCs with nonlocal operators show divergence over time.