Abstract

Graphs and graph analytics facilitate new approaches to machine learning. They also provide the ability to extract new insights from the same datasets as used in traditional machine learning experiments. For this reason, many researchers are seeking to exploit graph databases in pursuit of better performance for their predictive models. However, the construction of a graph from relational or flat models such as CSV files is not a straightforward transformation. A careful selection of nodes and relationships is required to ensure an optimal construction of the target graph. Overly large graphs can cause performance issues for a number of graph algorithms and thus, graph compression is an important part of the construction process. This research has 2 components: the usage of graphs to integrate multiple data sources and a graph transformation methodology to create the integrated schema and populate the graph. Our approach to validation uses link prediction and community detection graph analytics to evaluate the graphs built using our methodology.