Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node's global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.
Bootstrap percolation is a well-known model to study the spreading of rumors, new products or innovations on social networks. The empirical studies show that community structure is ubiquitous among various social networks. Thus, studying the bootstrap percolation on the complex networks with communities can bring us new and important insights of the spreading dynamics on social networks. It attracts a lot of scientists' attentions recently. In this letter, we study the bootstrap percolation on Erdős-Rényi networks with communities and observed second order, hybrid (both second and first order) and multiple hybrid phase transitions, which is rare in natural system. Moreover, we have analytically solved this system and obtained the phase diagram, which is further justified well by the corresponding simulations.
Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a set of influential spreaders, such that news propagating from them would lead to extensive coverage and minimal redundancy. The method utilizes the similarities between the segmented networks before percolation and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests of implementing the susceptible-infected-recovered model on Facebook and Enron email networks show that this method outperforms conventional centrality-based methods in terms of spreadability and coverage redundancy. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation in social networks.
Protecting citizens' lives from emergent accidents (e.g. traffic accidents) and diseases (e.g. heart attack) is of vital importance in urban computing. Every day many people are caught in emergent accidents or diseases and thus need ambulances to transport them to hospitals. In this paper, we propose a dynamic ambulance redeployment system to reduce the time needed for ambulances to pick up patients and to increase the probability of patients being saved in time. For patients in danger, every second counts. Specifically, whenever there is an ambulance becoming available (e.g. finishing transporting a patient to a hospital), our dynamic ambulance redeployment system will redeploy it to a proper ambulance station such that it can better pick up future patients. However, the dynamic ambulance redeployment is challenging, as when we redeploy an available ambulance we need to simultaneously consider each station's multiple dynamic factors. To trade off these multiple factors using handcrafted rules are almost impossible. To deal with this issue, we propose using a deep neural network, called deep score network, to balance each station's dynamic factors into one score, leveraging the excellent representation ability of deep neural networks. And then we propose a deep reinforcement learning framework to learn the deep score network. Finally, based on the learned deep score network, we provide an effective dynamic ambulance redeployment algorithm. Experiment results using data collected in real world show clear advantages of our method over baselines, e.g. comparing with baselines, our method can save ~100 seconds (~20%) of average pickup time of patients and improve the ratio of patients being picked up within 10 minutes from 0.786 to 0.838. With our method, people in danger can be better saved.
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