Skip to content

Discovering Interesting Patterns in Large Graph Cubes

Due to the increasing importance and volume of highly interconnected data, such as in social or information networks, a plethora of graph mining techniques have been designed to enable the analysis of such data. In this work, we focus on the mining of associations between entity features in networks. We model each entity feature as a dimension to be analyzed. Consequently we build our approach on top of the existing graph cube framework which is an extension of the concept of the data cube to networks. Our task is particularly challenging because it requires the analysis of both the initial multidimensional network and all its subsequent aggregate forms. As soon as we deal with a big data situation it is impossible for an analyst to consider manually all the possible views of the network data. The aim of this work is to design an algorithm for the discovery of interesting patterns in large graph cubes. Thus, instead of examining all the possible aggregations manually, the proposed technique leads the analyst to the interesting associations or patterns in the multidimensional network. Furthermore, we study the application of existing algorithms from the frequent itemset mining literature on graph data and propose a mapping between the two settings.

Florian Demesmaeker, Amine Ghrab, Siegfried Nijssen, Sabri Skhiri: Discovering interesting patterns in large graph cubes. 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 3322-3331.

Click here to access the paper.

Share on linkedin
Share on twitter
Share on email

Releated Posts

Master Thesis Offers 2022-2023

We offer master thesis supervised by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.
Read More

Congrats, Dr Ciortan!

We are super proud of our colleague Madalina who defended two weeks ago her PhD at the Université libre de Bruxelles. She studied unsupervised analysis of RNA sequencing protocols data, and brilliantly succeeded.
Read More