Proceedings ArticleDOI
Total Influence and Hybrid Simulation of Independent Cascade Model using Rough Knowledge Granules
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The paper defines a new theoretical measure Total Influence, of a node as well as a set of nodes in the social network, and proposes a new hybrid simulation methodology for the independent cascade model of diffusion to quantify the size of the spreading practically.Abstract:
The paper defines a new theoretical measure Total Influence, of a node as well as a set of nodes in the social network. Total influence uses probabilistic theory to obtain the expected size of the information spreading in the social network under the independent cascade model of diffusion. In order to quantify the size of the spreading practically, the paper proposes a new hybrid simulation methodology for the independent cascade model. The hybrid method uses rough set theory and defines rough knowledge agents around all the seed nodes from which the information is propagating. The lower approximation is calculated using the probabilistic approach, while the size of influence in the boundary region is quantified by Monte-Carlo simulation on a reduced network. The reduce network is formed by compacting all the nodes in the lower approximate region as a super-node. Experimental results on two synthetically generated directed network show that the hybrid method runs magnitude faster than its counterpart with a similar accuracy of the spreading size.read more
Citations
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Infectious Disease Modeling of Social Contagion in Networks
TL;DR: It is found that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time, suggesting that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight.
References
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Journal ArticleDOI
Maximizing the Spread of Influence through a Social Network
TL;DR: The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.
Proceedings ArticleDOI
Mining the network value of customers
TL;DR: It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
Journal ArticleDOI
A simple model of global cascades on random networks
TL;DR: It is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but anincreasingly heterogeneous degree distribution makes it less vulnerable.
Proceedings ArticleDOI
Cost-effective outbreak detection in networks
Jure Leskovec,Andreas Krause,Carlos Guestrin,Christos Faloutsos,Jeanne M. VanBriesen,Natalie S. Glance +5 more
TL;DR: This work exploits submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm and achieving speedups and savings in storage of several orders of magnitude.
Proceedings ArticleDOI
Efficient influence maximization in social networks
Wei Chen,Yajun Wang,Siyu Yang +2 more
TL;DR: Based on the results, it is believed that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time.
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