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...[Show more] model-free ways.
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-free approaches in accordance
with different context of diffusion. This helps us to obtain a more comprehensive understanding
of topic-related diffusion patterns. Both approaches provide a coherent
macroscopic view of global diffusion in terms of the strength and directionality of
influences among heterogeneous social networks. We find that the two approaches
provide similar results but with different perspectives, which in conjunction can help
better explain diffusion than either approach alone. They also suggest alternative options
as either or both of the approaches can be used appropriate to the real situations
of different application domains.
We expect that our proposed approaches provide ways to quantify and understand
cross-population diffusion trends at a macro level. Also, they can be applied
to a wide range of research areas such as social science, marketing, and even neuroscience,
for estimating dynamic influences among target regions or systems.
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