Graph Pattern Mining: consolidating models, systems, and abstractions

This text summarizes the key contributions of the dissertation entitled "Graph Pattern Mining: consolidating models, systems, and abstractions", approved in the Graduate Program in Computer Science of the Federal University of Minas Gerais (DCC/UFMG) Graph Pattern Mining (GPM) refers to a class of problems involving the processing of subgraphs extracted from larger graphs. Applications to GPM algorithms include querying subgraphs with given properties of interest, identifying motif structures in biological networks, among others. GPM algorithms are challenging to develop and thus, general-purpose GPM systems emerge as a solution to improve the user experience with such algorithms. In this dissertation we propose a primitive-based model for representing GPM algorithms, a distributed system implementing this model, and an extensive experimental study of popular algorithms used in GPM systems. We demonstrate empirically the effectiveness of our model by showing competitive performance without sacrificing the expressiveness of algorithms.