Scalable Community Detection on the Cloud: SCD made product

In our post Graph Databases research: Social community search we introduced the Scalable Community Detection (SCD) algorithm, born from the research work of the Sparksee team altogether with DAMA-UPC. The basic idea behind SCD is to search for the  number of transitive relations (triangles) and understand how they structure to form cohesive and structured communities. This leads to a more fast and accurate communities finder: faster than Louvain algorithm (fastest so far) and more accurate that Oslom algorithm (which had the highest quality so far).

Now we can proudly announce that we are taking the first steps towards commercialization of the SCD thanks to a Technology Transfer Project (TTP) provided by TETRACOM FP7 project. The mission of TETRACOM is to boost European academia-to-industry technology transfer (TT), and its main tool are the TTPs that provide partial funding of academia-industry collaborations that bring concrete R&D results into industrial use. In this case, the industrial partner Sparsity Technologies and the research partner Universitat Politècnica de Catalunya are working to make Scalable Community Detection on the Cloud (SCDC) a reality.

The general idea behind SCDC is to provide an scalable cloud service that when a user introduces to our system his network  he is going to get in the shortest time the most accurate communities inherently there. For those curious about our technology choices we are going to use a scalable architecture using Golang and Revel for the REST API, MongoDB to store the information and NSQ to process distributed queues.

SCDC

 

 

 

 

 

 

 

 

 

 

 

Stay tuned for more information about the project. Remember that you can get the SCD algorithm at Github.

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