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Real diffusion networks are complex and dynamic, since underlying social structures are not only far-reaching beyond a single homogeneous system but also frequently changing with the context of diffusion. Thus, studying topic-related diffusion across multiple social systems is important for a better understanding of such realistic situations. Accordingly, this thesis focuses on uncovering topic-related diffusion dynamics across heterogeneous social networks in both model-driven and model-free<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25911/5d70f241bce00">doi:10.25911/5d70f241bce00</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dpnafp643vb33dh72y6gjbox7q">fatcat:dpnafp643vb33dh72y6gjbox7q</a> </span>
more »... ys. We first conduct empirical studies for analyzing diffusion phenomena in real world systems, such as new diffusion in social media and knowledge transfer in academic publications. We observe that large diffusion is more likely attributed to interactions between heterogeneous social networks as if they were in the same networks. Thus, external influences from out-of-the-network sources, as observed in previous work, need to be explained with the context of interactions between heterogeneous social networks. This observation motivates our new conceptual framework for cross-population diffusion, which extends the traditional diffusion mechanism to a more flexible and general one. Second, we propose both model-driven and model-free approaches to estimate global trends of information diffusion. Based on our conceptual framework, we propose a model-driven approach which allows internal influence to reach heterogeneous populations in a probabilistic way. This approach extends a simple and robust mass action diffusion model by incorporating the structural connectivity and heterogeneity of real-world networks. We then propose a model-free approach using informationtheoretic measures with the consideration of both time-delay and memory effects on diffusion. In contrast to the model-driven approach, this model-free approach does not require any assumptions on dynamic social interactions in the real world, providing the benefits of quantifying nonlinear dynamics of complex systems. Finally, we compare our model-driven and model-fre [...]
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