With the emergence of data and network science, the efficient processing of graph data is becoming a key factor in querying, analyzing, and visualizing connected data. Furthermore, data represented using graph have not only grown in size, but have also become richer and more dynamic. Consequently, the development of methods to store and analyze large volumes of graph data efficiently is more necessary than ever.
In this context, a variety of solution for computing various graph analytics efficiently has been proposed. However, most existing methods focus on graphs from a specific domain. We are interested in developing solution to compute structural analytics and common traits among different types of graphs from different domains. We also investigate how certain properties of an underlying graph influence the performance of certain indexing and query processing methods, and we explore ways to apply such methods on a variety of graphs that share the same traits regardless of the domain.