Although the many forms of modern social media have become major channels for the dissemination of information, they are becoming overloaded because of the rapidly-expanding number of information feeds. We analyze the expanding user-generated content
in Sina Weibo, the largest micro-blog site in China, and find evidence that popular messages often follow a mechanism that differs from that found in the spread of disease, in contrast to common believe. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that in contrast to epidemics, for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her. Thus the greater the number of friends an individual has the greater the number of repeated contacts needed to spread the message, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold. This is in contrast to the zero threshold that conventional wisdom derives from disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received, leading to only a handful of information contents that can be widely spread throughout the population.
We study spectra of directed networks with inhibitory and excitatory couplings. We investigate in particular eigenvector localization properties of various model networks for different value of correlation among their entries. Spectra of random netwo
rks, with completely uncorrelated entries show a circular distribution with delocalized eigenvectors, where as networks with correlated entries have localized eigenvectors. In order to understand the origin of localization we track the spectra as a function of connection probability and directionality. As connections are made directed, eigenstates start occurring in complex conjugate pairs and the eigenvalue distribution combined with the localization measure shows a rich pattern. Moreover, for a very well distinguished community structure, the whole spectrum is localized except few eigenstates at boundary of the circular distribution. As the network deviates from the community structure there is a sudden change in the localization property for a very small value of deformation from the perfect community structure. We search for this effect for the whole range of correlation strengths and for different community configurations. Furthermore, we investigate spectral properties of a metabolic network of zebrafish, and compare them with those of the model networks.
Memory is an indispensable element for computer besides logic gates. In this Letter we report a model of thermal memory. We demonstrate via numerical simulation that thermal (phononic) information stored in the memory can be retained for a long time
without being lost and more importantly can be read out without being destroyed. The possibility of experimental realization is also discussed.
The thermal conductivity of silicon nanowires (SiNWs) is investigated by molecular dynamics (MD) simulation. It is found that the thermal conductivity of SiNWs can be reduced exponentially by isotopic defects at room temperature. The thermal conducti
vity reaches the minimum, which is about 27% of that of pure 28Si NW, when doped with fifty percent isotope atoms. The thermal conductivity of isotopic-superlattice structured SiNWs depends clearly on the period of superlattice. At a critical period of 1.09 nm, the thermal conductivity is only 25% of the value of pure Si NW. An anomalous enhancement of thermal conductivity is observed when the superlattice period is smaller than this critical length. The ultra-low thermal conductivity of superlattice structured SiNWs is explained with phonon spectrum theory.
