Abstract
Graphs can model a variety of datasets, spanning from social networks to text. Because of the diversity and relevance of applications, it is important to develop algorithms that are well-suited for mining real-world graphs. In this talk, I will give several results on mining dense subgraphs, where density is used to measure the importance and the cohesiveness of a subgraph. Next, I will demonstrate how dense subgraphs can be used for real-world disaster detection. For this purpose, we construct representative graphs from informal text, such as posts from the social media platform Twitter. Social media differ from traditional media through an important characteristic: every user can contribute to a story with its own personal experience. This richness of content can help in a situation of crisis when authorities want to access more information about affected areas. Lastly, I will discuss text mining in another social media platform, the online forum Reddit. Discussions on online forums are the result of both the dynamics of a conversation and the features of the underlying platform. My focus in this work is on finding patterns in conversations and on developing new tools that better capture the semantics of an online discussion.
Bio
Oana Balalau, currently a post-doc at MPI Saarbrucken,
(http://people.mpi-inf.mpg.de/~obalalau/)
