Graph Databases research: Social community search

We would like today to share an interesting article recently published at DZone’s portal about the research the Sparksee team altogether with DAMA-UPC is working on about social community search.

Community search is a very important aspect of Social Network (SN) analysis. Communities are defined as tightly related groups of people, who, for instance, communicate or interact with the members of the community more intensely than with the rest of the population. SNs are complex representations of society and understanding their structure is key to be able to find those communities accurately.

Lately our research has focused on understanding the nature of social communities in order to use that knowledge to build a more accurate and fast communities finder in very large graphs. As a result of that on March 2014 at the WWW’14 we presented The Scalable Community Detection (SCD) algorithm.

SCD exploits one of the basic properties of SNs; they have a number of transitive relations (triangles) larger than other types of networks. The number of triangles in a specific community will be larger than in the whole SN. The basic idea of SCD is to search for those triangles and understand how they structure to form cohesive and structured communities around those triangles.


Comparing SCD results to other algorithms in the State of the Art, we can claim that it is fastest that Louvain algorithm (fastest so far) and more accurate that Oslom algorithm (which had the highest quality so far). Check the Dzone article to read more details about this claim and check DAMA-UPC website to download the code of this algorithm.

Are you interested in using Sparksee for your research? Go ahead and request your free license under our Research program.

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