RDBMS are not very flexible in dealing with big changes and also they do not have ability of performing complex data manipulations like detect patterns, find relationship, predictive modeling etc. Graph databases are powerful tool to store, represent and manipulate huge amount of complex information. If your data contains a lot of many-to-many relationships, if recursive self-joins are too costly or limiting to your application and scaling needs, and/or your primary objective is quickly finding connections, patterns and relationships then graph database is a perfect fit.
Graph database uses graph structures with nodes, edges, and properties to represent and store data. Compared with relational databases, graph databases are often faster for associative data sets, and map more directly to the structure of object oriented applications. They are a powerful tool for graph-like queries, for example computing the shortest path between two nodes in the graph. They can scale more naturally to large data sets as they do not typically require expensive join operations. They depend less on a rigid schema, hence they are more suitable to manage ad-hoc and changing data with evolving schema. Database integrity and consistency rules can be conveniently implemented and enforced through API, quite better than using relational model.