282 research outputs found
Efficient detection of contagious outbreaks in massive metropolitan encounter networks
Physical contact remains difficult to trace in large metropolitan networks,
though it is a key vehicle for the transmission of contagious outbreaks.
Co-presence encounters during daily transit use provide us with a city-scale
time-resolved physical contact network, consisting of 1 billion contacts among
3 million transit users. Here, we study the advantage that knowledge of such
co-presence structures may provide for early detection of contagious outbreaks.
We first examine the "friend sensor" scheme --- a simple, but universal
strategy requiring only local information --- and demonstrate that it provides
significant early detection of simulated outbreaks. Taking advantage of the
full network structure, we then identify advanced "global sensor sets",
obtaining substantial early warning times savings over the friends sensor
scheme. Individuals with highest number of encounters are the most efficient
sensors, with performance comparable to individuals with the highest travel
frequency, exploratory behavior and structural centrality. An efficiency
balance emerges when testing the dependency on sensor size and evaluating
sensor reliability; we find that substantial and reliable lead-time could be
attained by monitoring only 0.01% of the population with the highest degree.Comment: 4 figure
Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation
Path diversity improves the identification of influential spreaders
Identifying influential spreaders in complex networks is a crucial problem
which relates to wide applications. Many methods based on the global
information such as -shell and PageRank have been applied to rank spreaders.
However, most of related previous works overwhelmingly focus on the number of
paths for propagation, while whether the paths are diverse enough is usually
overlooked. Generally, the spreading ability of a node might not be strong if
its propagation depends on one or two paths while the other paths are dead
ends. In this Letter, we introduced the concept of path diversity and find that
it can largely improve the ranking accuracy. We further propose a local method
combining the information of path number and path diversity to identify
influential nodes in complex networks. This method is shown to outperform many
well-known methods in both undirected and directed networks. Moreover, the
efficiency of our method makes it possible to be applied to very large systems.Comment: 6 pages, 6 figure
Identifying the influential spreaders in multilayer interactions of online social networks
Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations
A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data
Epidemic outbreaks are an important healthcare challenge, especially in
developing countries where they represent one of the major causes of mortality.
Approaches that can rapidly target subpopulations for surveillance and control
are critical for enhancing containment processes during epidemics.
Using a real-world dataset from Ivory Coast, this work presents an attempt to
unveil the socio-geographical heterogeneity of disease transmission dynamics.
By employing a spatially explicit meta-population epidemic model derived from
mobile phone Call Detail Records (CDRs), we investigate how the differences in
mobility patterns may affect the course of a realistic infectious disease
outbreak. We consider different existing measures of the spatial dimension of
human mobility and interactions, and we analyse their relevance in identifying
the highest risk sub-population of individuals, as the best candidates for
isolation countermeasures. The approaches presented in this paper provide
further evidence that mobile phone data can be effectively exploited to
facilitate our understanding of individuals' spatial behaviour and its
relationship with the risk of infectious diseases' contagion. In particular, we
show that CDRs-based indicators of individuals' spatial activities and
interactions hold promise for gaining insight of contagion heterogeneity and
thus for developing containment strategies to support decision-making during
country-level pandemics
Robustness and edge addition strategy of air transport networks : a case study of 'the Belt and Road'
Air transportation is of great importance in "the Belt and Road" (the B&R) region. The achievement of the B&R initiative relies on the availability, reliability, and safety of air transport infrastructure. A fundamental step is to find the critical elements in network performance. Considering the uneven distributions of population and economy, the current literature focusing on centrality measures in unweighted networks is not sufficient in the B&R region. By differentiating power and centrality in the B&R region, our analysis leads to two conclusions: (1) Deactivating powerful nodes causes a larger decrease in efficiency than deactivating central nodes. This indicates that powerful nodes in the B&R region are more critical than central nodes for network robustness. (2) Strategically adding edges between high powerful and low powerful nodes can enhance the network's ability to exchange resources efficiently. These findings can be used to adjust government policies for air transport configuration to achieve the best network performance and the most cost effective
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