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What and Who
Title:Structure and Dynamics of Diffusion Networks
Speaker:Manuel Gomez Rodriguez
coming from:Max Planck Institute for Intelligent Systems
Speakers Bio: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/

Event Type:SWS Colloquium
Visibility:SWS, RG1
We use this to send out email in the morning.
Level:AG Audience
Language:English
Date, Time and Location
Date:Thursday, 13 March 2014
Time:10:30
Duration:90 Minutes
Location:Kaiserslautern
Building:G26
Room:113
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.
Contact
Name(s):Claudia Richter
Phone:9303 9103
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:YesTo Location:Saarbr├╝cken
To Building:E1 5To Room:029
Meeting ID:
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Attachments, File(s):
  • Claudia Richter, 03/11/2014 10:50 AM -- Created document.