1,429 research outputs found
Correlated Cascades: Compete or Cooperate
In real world social networks, there are multiple cascades which are rarely
independent. They usually compete or cooperate with each other. Motivated by
the reinforcement theory in sociology we leverage the fact that adoption of a
user to any behavior is modeled by the aggregation of behaviors of its
neighbors. We use a multidimensional marked Hawkes process to model users
product adoption and consequently spread of cascades in social networks. The
resulting inference problem is proved to be convex and is solved in parallel by
using the barrier method. The advantage of the proposed model is twofold; it
models correlated cascades and also learns the latent diffusion network.
Experimental results on synthetic and two real datasets gathered from Twitter,
URL shortening and music streaming services, illustrate the superior
performance of the proposed model over the alternatives
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
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