Julio Cesar Louzada Pinto

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We develop in this paper a trend detection algorithm, designed to find trendy topics being disseminated in a social network. We assume that the broadcasts of messages in the social network is governed by a self-exciting point process, namely a Hawkes process, which takes into consideration the real broadcasting times of messages and the interaction between(More)
We define in this paper a general Hawkes-based framework to model information diffusion in social networks. The proposed framework takes into consideration the hidden interactions between users as well as the interactions between contents and social networks, and can also accommodate dynamic social networks and various temporal effects of the diffusion,(More)
We introduce a new model of opinion dynamics in which agents interact with each other about several distinct opinions/contents. In most of the literature about opinion dynamics, agents perform convex combinations of other agents’ opinions. In our framework, a competition between opinions takes place: agents do not exchange all their opinions with each(More)
We model in this work dissemination of contents and competition between sources of contents in social networks composed of a given number of resources (channels or links) used by sources for dissemination of their contents, in the case where some of these resources may be lost during the propagation process, corresponding to the so-called Susceptible(More)
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