# 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

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