Knowledge Extraction from Biological and Social Graphs

Many problems from real life deal with the generation of enormous, varied, dynamic, and interconnected datasets coming from different and heterogeneous sources. Analysing large volumes of data makes it possible to generate new knowledge useful for making more informed decisions, in business and beyond. From personalising customer communication to streamlining production processes, via flow and emergency management, Big Data Analytics has an impact on all processes. The potential uses of Big Data go much further: two of the largest sources of data are including individual traders’ purchasing history, the use of Biological Networks for disease prediction or the reduction and study of Biological Networks. From a computer science point of view, the networks are graphs with various characteristics specific to the application domain. This PhD Thesis focuses on the proposal of novel knowledge extraction techniques from large graphs, mainly based on Big Data methodologies. Two application contexts are considered and three specific problems have been solved: Social data, for the optimization of advertising campaigns, the comparison of user profiles, and neighborhood analysis. Biological and Medical data, with the final aim of identifying biomarkers for diagnosis, treatment, prognosis, and prevention of diseases.