GraphDasein has the following key goals, and spans a wide spectrum of questions that range from systems to algorithm design, and graph theory.
Scalable Algorithmics: How do we scale graph mining to peta-sized graphs?
Data-driven Algorithmics: Can we exploit properties of the input to solve computationally challenging, including NP-hard, problems?
Modeling networks: How do we model social networks, or some properties of them using random graphs? Can we use these models to design efficient algorithms?
Harnessing networks: How can we better leverage networks in data mining and machine learning?
Research Projects & Software
You can find out more about GraphDasein from the following Web pages (soon to be public).
- Anomaly detection
- Counting Motifs
- Dense subgraph discovery
- Influence Maximization
- Large-Scale Graph Processing
- Mining uncertain graphs
- Opinion Dynamics
- Random graphs
- Social media
- Community detection
- Time-evolving networks