The impact that information diffusion has on epidemic spreading has recently attracted much attention. As a disease begins to spread in the population, information about the disease is transmitted to others, which in turn has an effect on the spread
of disease. In this paper, using empirical results of the propagation of H7N9 and information about the disease, we clearly show that the spreading dynamics of the two-types of processes influence each other. We build a mathematical model in which both types of spreading dynamics are described using the SIS process in order to illustrate the influence of information diffusion on epidemic spreading. Both the simulation results and the pairwise analysis reveal that information diffusion can increase the threshold of an epidemic outbreak, decrease the final fraction of infected individuals and significantly decrease the rate at which the epidemic propagates. Additionally, we find that the multi-outbreak phenomena of epidemic spreading, along with the impact of information diffusion, is consistent with the empirical results. These findings highlight the requirement to maintain social awareness of diseases even when the epidemics seem to be under control in order to prevent a subsequent outbreak. These results may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.
can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from furthe
r spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice give a visual representation about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, oscillatory, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading.
Recently, information transmission models motivated by the classical epidemic propagation, have been applied to a wide-range of social systems, generally assume that information mainly transmits among individuals via peer-to-peer interactions on soci
al networks. In this paper, we consider one more approach for users to get information: the out-of-social-network influence. Empirical analyses of eight typical events diffusion on a very large micro-blogging system, emph{Sina Weibo}, show that the external influence has significant impact on information spreading along with social activities. In addition, we propose a theoretical model to interpret the spreading process via both internal and external channels, considering three essential properties: (i) memory effect; (ii) role of spreaders; and (iii) non-redundancy of contacts. Experimental and mathematical results indicate that the information indeed spreads much quicker and broader with mutual effects of the internal and external influences. More importantly, the present model reveals that the event characteristic would highly determine the essential spreading patterns once the network structure is established. The results may shed some light on the in-depth understanding of the underlying dynamics of information transmission on real social networks.
Interactions between search and recommendation have recently attracted significant attention, and several studies have shown that many potential applications involve with a joint problem of producing recommendations to users with respect to a given q
uery, termed $Collaborative$ $Retrieval$ (CR). Successful algorithms designed for CR should be potentially flexible at dealing with the sparsity challenges since the setup of collaborative retrieval associates with a given $query$ $times$ $user$ $times$ $item$ tensor instead of traditional $user$ $times$ $item$ matrix. Recently, several works are proposed to study CR task from users perspective. In this paper, we aim to sufficiently explore the sophisticated relationship of each $query$ $times$ $user$ $times$ $item$ triple from items perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query-user pair. In addition, we suggest to employ a recently proposed scalable ranking learning algorithm, namely BPR, to optimize the state-of-the-art approach, $Latent$ $Collaborative$ $Retrieval$ model, instead of the original learning algorithm. The experimental results on two real-world datasets, (i.e. emph{Last.fm}, emph{Yelp}), demonstrate the efficiency and effectiveness of our proposed approach.
In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-explored topic is how those data, dominated by application logs, are colle
cted and structured to begin with. In this paper, we present Twitters production logging infrastructure and its evolution from application-specific logging to a unified client events log format, where messages are captured in common, well-formatted, flexible Thrift messages. Since most analytics tasks consider the user session as the basic unit of analysis, we pre-materialize session sequences, which are compact summaries that can answer a large class of common queries quickly. The development of this infrastructure has streamlined log collection and data analysis, thereby improving our ability to rapidly experiment and iterate on various aspects of the service.
The two-dimensional multifractal detrended fluctuation analysis is applied to reveal the multifractal properties of the fracture surfaces of foamed polypropylene/polyethylene blends at different temperatures. Nice power-law scaling relationship betwe
en the detrended fluctuation function $F_{q}$ and the scale $s$ is observed for different orders $q$ and the scaling exponent $h(q)$ is found to be a nonlinear function of $q$, confirming the presence of multifractality in the fracture surfaces. The multifractal spectra $f(alpha)$ are obtained numerically through Legendre transform. The shape of the multifractal spectrum of singularities can be well captured by the width of spectrum $Deltaalpha$ and the difference of dimension $Delta f$. With the increase of the PE content, the fracture surface becomes more irregular and complex, as is manifested by the facts that $Deltaalpha$ increases and $Delta f$ decreases from positive to negative. A qualitative interpretation is provided based on the foaming process.
