Description

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