District Data Labs - Graph Analytics Over Relational Datasets with Python

The analysis of interconnection structures of entities connected through relationships has proven to be of immense value in understanding the inner-workings of networks in a variety of different data domains including finance, health care, business, computer science, etc. These analyses have emerged in the form of Graph Analytics -- the analysis of the characteristics in these graph structures through various graph algorithms. Some examples of insights offered by graph analytics include finding clusters of entities closely connected to each-other, calculating optimal paths between entities (the definition of optimal depending on the dataset and use case), understanding the hierarchy of entities within an organization as well as figuring out the impact each entity has inside the network. Graph structured data is a specialized type of dataset in terms of the way we need to access it; therefore it needs to be stored in ways that complements these access patterns. This has sparked the emergence of a wide variety of specialized graph databases such as Neo4j, OrientDB, Titan etc.