Graph Databases research: Synthetic graph data generators

Sparsity Technologies is very interested not only in the graph database technology itself, but also in all the applications that can take advantage of Sparksee. One of the most popular ones is the analysis of social media data represented as graphs. For this topic, our research team is collaborating with social data experts in DAMA of the Universitat Politècnica de Catalunya.

Our latest work was presented in the Graph Data-management Experiences & Systems (GRADES) Workshop in conjunction with DAMA-UPC. There, we presented our experiencies with synthetic graph data generators. Such generators build arbitrarely large graphs that aim at simulating the structure of social networks. We experimented with two generators LFR (by Lancichinetti et al.) and the LDBC data generator. The former provides the network structure of a social network, while the latter builds a full social network with attributes and posts that will be used in the LDBC benchmarks.

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In our paper, we observe that LFR is able to mimic some of the properties of real networks, though the distribution of triangles in the network differs. On the other hand, the LDBC data generator provides a much more accurate network, that simulates with large precision the community aspect of real networks. Such data generators are very valuable for Sparksee’s development since they allow us to generate huge graphs to test our database technology thoroughly. Read more information about the creation of synthetic graph data generations in our paper here.

 

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