• Are you a social scientist who would like to find primary influencers over a friendship and message network?
• Are you an investigator who would like to track suspicious alliances in a network of rogue organizations and individuals?
• Are you a network biologist who would like to find relationships between genes in a chromosomal region and a family of diseases?
• Did you know that Cisco estimates 50 billion connected devices by 2020?

For these and many other tasks it is important to view your data as a graph (network) of nodes or vertices that represent objects and edges that represent relationships between them. For many application areas like sensor networks, the graphs may be large and have a billion nodes and edges. For applications like situation monitoring, they may represent a thousand types of entities and relationships. For telecommunication applications, the connections may vary with time, and some entities can be very densely connected to each other.  

Graph Analytics is a rapidly developing area where a combination of graph-theoretic, statistical and database techniques are applied to model, store, retrieve, and perform analyses on graph-structured data. These techniques enable us to understand the structure of a network and how it changes in different conditions, find paths between pairs of entities that satisfy different constraints, identify clusters or closely interacting subgroups inside a graph, find subgraphs that are similar to a given pattern and so forth.

The Graph Analytics Course will offer a broad overview of the field as well as a deep insight into specific analytical techniques. It will introduce how to model a problem into a graph database, and perform analytical tasks over it in a scalable manner. Based on a fully worked out use case, the course will provide a comprehensive understanding of how to apply graph analytics to your applications.