[gov3009-l] Applied Statistics - 11/11 - Manuel Gomez-Rodriguez

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Nov 9 09:42:30 EST 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Manuel
Gomez Rodriguez*, tenure-track research group director at the Max Planck
Institute for Software Systems. He will be presenting work entitled *COEVOLVE:
A Joint Point Process Model for Information Diffusion and Network
Co-evolution**.*  Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/11112015-manuel-gomez-rodriguez-harvard-title-coming>
.

Manuel is pioneering an educational and research program at Max Planck in
computational social sciences, and is very excited to learn from our
experiences. Additionally, his work in machine learning, especially
learning processes over networks, has received numerous awards recently.
Those interested in meeting with Manuel are welcome to sign up here
<https://docs.google.com/spreadsheets/d/1U027d0qTmZIjw6HEf1S8rK7XTMNKt8LClduoIbbBEEE/edit#gid=0>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: COEVOLVE: A Joint Point Process Model for Information Diffusion and
Network Co-evolution

Information diffusion in online social networks is affected by the
underlying network topology, but it also has the power to change it. Online
users are constantly creating new links when exposed to new information
sources, and in turn these links are alternating the way information
spreads. However, these two highly intertwined stochastic processes,
information diffusion and network evolution, have been predominantly
studied separately, ignoring their co-evolutionary dynamics. In this talk,
we introduce a temporal point process model, COEVOLVE, for such joint
dynamics, allowing the intensity of one process to be modulated by that of
the other. This model allows us to efficiently simulate interleaved
diffusion and network events, and generate traces obeying common diffusion
and network patterns observed in real-world networks. Furthermore, we also
develop a convex optimization framework to learn the parameters of the
model from historical diffusion and network evolution traces. We
experimented with both synthetic data and data gathered from Twitter, and
show that our model provides a good fit to the data as well as more
accurate predictions than alternatives.

-- 
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
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