34 research outputs found
Control Efficacy on COVID-19
We proposed a Monte-Carlo method to estimate temporal reproduction number
without complete information about symptom onsets of all cases. Province-level
analysis demonstrated the huge success of Chinese control measures on COVID-19,
that is, provinces' reproduction numbers quickly decrease to <1 by just one
week after taking actions.Comment: 18 pages, 5 figures, 1 tabl
Identifying influential spreaders by weighted LeaderRank
Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank (Lü et al., 2011). According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders; (ii) the higher tolerance to noisy data; and (iii) the higher robustness to intentional attacks
The Role of Taste Affinity in Agent-Based Models for Social Recommendation
In the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope — who thus act as selective filters in social recommendation
Adaptive social recommendation in a multiple category landscape
People in the Internet era have to cope with the information overload, striving to find what they are interested in, and usually face this situation by following a limited number of sources or friends that best match their interests. A recent line of research, namely adaptive social recommendation, has therefore emerged to optimize the information propagation in social networks and provide users with personalized recommendations. Validation of these methods by agent-based simulations often assumes that the tastes of users can be represented by binary vectors, with entries denoting users' preferences. In this work we introduce a more realistic assumption that users' tastes are modeled by multiple vectors. We show that within this framework the social recommendation process has a poor outcome. Accordingly, we design novel measures of users' taste similarity that can substantially improve the precision of the recommender system. Finally, we discuss the issue of enhancing the recommendations' diversity while preserving their accurac
Enhancing topology adaptation in information-sharing social networks
The advent of Internet and World Wide Web has led to unprecedent growth of
the information available. People usually face the information overload by
following a limited number of sources which best fit their interests. It has
thus become important to address issues like who gets followed and how to allow
people to discover new and better information sources. In this paper we conduct
an empirical analysis on different on-line social networking sites, and draw
inspiration from its results to present different source selection strategies
in an adaptive model for social recommendation. We show that local search rules
which enhance the typical topological features of real social communities give
rise to network configurations that are globally optimal. These rules create
networks which are effective in information diffusion and resemble structures
resulting from real social systems