Graph Analytics is a rapidly developing area of research where a combination of graph-theoretic, statistical, and database techniques are applied to model, store, retrieve, and performance analyses on graph-structured data. These techniques enable researchers 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, or find subgraphs that are similar to a given pattern.
For these and many other tasks it is important to view ones data as a graph (network) of nodes or vertices that represent objects and edges that represent relationships between them. For many application areas such as sensor networks, the graphs may be large and have a billion nodes and edges. For applications such as 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.
SDSC’s Graph Analytics Boot Camps offer a broad overview of the field as well as a deep insight into specific analytical techniques. Participants will learn 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, these workshops will provide participants with a comprehensive understanding of how to apply graph analytics to your applications.