Campus Event Calendar
Event Entry
What and Who
Structure and Dynamics of Diffusion Networks
Manuel Gomez Rodriguez
Max Planck Institute for Intelligent Systems
SWS Colloquium
Manuel Gomez Rodriguez is a Research Scientist at Max Planck Institute for Intelligent Systems. Manuel develops machine learning
and large-scale data mining methods for the analysis and modeling of large real-world networks and processes that take place over them.
He is particularly interested in problems motivated by the Web and social media and has received several recognitions for his research,
including an Outstanding Paper Award at NIPS'13 and a Best Research Paper Honorable Mention at KDD'10. Manuel holds a PhD in Electrical
Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid (Spain). You can find
more about him at http://people.tuebingen.mpg.de/manuelgr/
SWS, RG1
AG Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Thursday, 13 March 2014
10:30
90 Minutes
G26
113
Kaiserslautern
Abstract
Networks represent a fundamental medium for spreading and diffusion of various types of information, behavior and rumors. However, observing a diffusion process often reduces to noting when nodes (people, blogs, etc.) reproduce a piece of information, adopt a behavior, buy a product, or, more generally, adopt a contagion. We often observe where and when but not how or why contagions propagate through a network. The mechanism underlying the process is hidden. However, the mechanism is of outstanding interest in all cases, since understanding diffusion is necessary for predicting meme propagation, stopping rumors, or maximizing sales of a product.
In this talk, I will present a flexible probabilistic model of diffusion over networks that makes minimal assumptions about the physical, biological or cognitive mechanisms responsible for diffusion. This is possible since the model is data-driven and relies primarily on the visible temporal traces that diffusion processes generate. I apply the model to information diffusion among 3.3 million blogs and mainstream media sites during a one year period. The model allows us to predict future events, it sheds light on the hidden underlying structure and temporal dynamics of diffusion, and provides
insights into the positions and roles various nodes play in the diffusion process.
In this talk, I will present a flexible probabilistic model of diffusion over networks that makes minimal assumptions about the physical, biological or cognitive mechanisms responsible for diffusion. This is possible since the model is data-driven and relies primarily on the visible temporal traces that diffusion processes generate. I apply the model to information diffusion among 3.3 million blogs and mainstream media sites during a one year period. The model allows us to predict future events, it sheds light on the hidden underlying structure and temporal dynamics of diffusion, and provides
insights into the positions and roles various nodes play in the diffusion process.
Contact
Claudia Richter
9303 9103
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Video Broadcast
Yes
Saarbrücken
E1 5
029
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Claudia Richter, 03/11/2014 10:50 -- Created document.