According to Bill Schmarzo, author at InFocus, “Graph analytics leverage graph structures to understand, codify, and visualize relationships that exist between people or devices in a network. Graph analytics, built on the mathematics of graph theory, is used to model pairwise relationships between people, objects, or nodes in a network. It can uncover insights about the strength and direction of the relationship.”
Analytical techniques can be used on graphs to highlight relationships between various entities and find out complex behavorial patterns, influencers in a group, and similar conclusions.
The crowning glory of this types of analytics courses is its uncanny ability to discover data. It tweaks out unknown links in an era when the primary discussions hovering around big data are about answering specific questions. Those unknown links might be pointers to hidden treasure when one doesn’t even know what should have been asked for. Patterns emerging out of multiple datasets give a wholesome picture, enabling users to have a – what we can say a 360 degree view, something that will clearly facilitate smarter decision making.
How are graphs better?
Graphs are dynamic, non-planar, have non-local communications, and dynamic work is performed by crawlers or autonomous agents. Work modifies data in many places which deals with the limitation of local data used in scientific grids.
Graph analytics makes comparison ‘many-to-many,’ unlike relational analytics that works ‘one-to-one’ or even ‘one-to-many’. For instance, if relational analytics can locate one or many friends of a person, a graph can locate friends of those many persons and also show whether they are friends with each other – something where relational analytics falter. If Facebook enters your mind while reading this, you are thinking in the right direction. It is an example of how graph technology gleans information on the basis of relationships.
As for examples of applications where graph analytics can do more than suggest ‘people you may know’, see here for an illustrated article of how it can be used to crack credit card fraud rings.
While relational analytics are capable of providing insights into structured, unchanging data stored in columns and tables, unstructured data that is constantly changing can be interpreted much better by graphs that provide a different analytic lens to view information deeply. The result: deeper insights and more accurate predictions.
There is a fundamental difference between big data graph analytics and big data science. The algorithms, challenges, and even hardware requirements differ significantly. Conventional database systems are insufficient for graphs, though data parallel graph crawls are of a much faster magnitude and hence are capable of repaying the investment that would be involved in arranging new systems.
However, it is also true that graph analytics are not hailed as a replacement for relational analytics. Both have their unique applications and will be needed by companies since each holds its own sway depending upon the scenario.
