Practical Course Advanced Networking (Winter 2011/2012): Difference between revisions

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| Group function in mobile social networks and its evaluation
| Mobile effects in online social networks
| [http://www.net.informatik.uni-goettingen.de/people/konglin_zhu Konglin Zhu]
| [http://www.net.informatik.uni-goettingen.de/people/konglin_zhu Konglin Zhu]
| [http://www.winterwell.com/software/jtwitter/] [http://www.twitter.com/] [http://www.cl.cam.ac.uk/~ph315/publications/goose_final_socialComWorkshop.pdf]
| [http://www.twitter.com/]  
| Complete the design of group function in a mobile social networks and evaluate the performance using crawled online/mobile social network datast.
| Evaluating the influence of mobile phone in online social networks, including social graph analysis. Analysize the offline social network data and compare the difference of mobile effects in such two scenarios.
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| Interest graph modeling and analysis
| Classify the influence of users from perspective of information diffusion and social graphs
| [http://www.net.informatik.uni-goettingen.de/people/konglin_zhu Konglin Zhu]
| [http://www.net.informatik.uni-goettingen.de/people/konglin_zhu Konglin Zhu]
| [http://blog.assetmap.com/2010/11/social-web/why-the-interest-graph-will-reshape-social-networks-and-the-next-generation-of-internet-business/]
| Classify users by his/her diffused information and social graph. The purpose of this project is to find out the most powerful node in the network from the perspective of information diffusion and social graph. We will use machine learning algorithms to classify unlabeled nodes in the social networks.
| In social networks, people sharing same interests or public pages, which construct interest graph. This kind of interest orientated graph can not only create a valuable exchange for intermation commerce, but also improve how people learn, discover, share and communicate. It provides a new path of content sharing. Interest graph is "interest" objected. People may not know each other but sharing same interest can be in a same interest graph. We provide you a interested based dataset to analyize the interest graph. It includes the interest graph construction, interest graph degree, distribution, and the correlation between interest graph and social graph.
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