Extraction, Identification, and Ranking of Network Structures from Data Sets

Networks are widely used to model a variety of complex, often multi-disciplinary, systems in which the relationships between their sub-parts play a significant role. In particular, there is extensive research on the topological properties associated with their structure as this allows the analysis of the overall behaviour of such networks. However, extracting networks from structured and unstructured data sets raises several challenges, including addressing any inconsistency present in the data, as well as the difficulty in investigating their properties especially when the topological structure is not fully determined or not explicitly defined. In this paper, we propose a novel method to address the automated identification, assessment and ranking of the most likely structure associated with networks extracted from a variety of data sets. More specifically, our approach allows to mine data to assess whether their associated networks exhibit properties comparable to well-known structures, namely scale-free, small world and random networks. The main motivation is to provide a toolbox to classify and analyse real-world networks otherwise difficult to fully assess due to their potential lack of structure. This can be used to investigate their dynamical and statistical behaviour which would potentially lead to a better understanding and prediction of the properties of the system (s) they model. Our initial validation shows the potential of our method providing relevant and accurate results.