Graph analytics is comparatively a newer model of analytics, which, as the name suggests, analyzes the organization’s metadata in a graph-based format. It is usually used for a structured, unstructured, numeric, or visual form of data to divulge insights that help the organizations in precise and fast decision-making. In her article for ‘Start It Up,’ Aasaveri Kaley talks about graph analytics and how it differs from other forms of analytics.
What Is Graph Analytics?
Graph analytics, also known as network analysis, is an analytical entity based on the interrelations between customers, products, operations, or devices. It enables the companies to gather an instructive insight in search engine optimization, fraud detection, supply chain, and marketing. As Oracle mentions, graph analytics helps us store, manage, and query data in the form of a graph. The entities become vertices, and relationships are known as edges.
How Is Graph Analytics Different from the Rest?
Graph analytics doesn’t rely on statistics and assesses algorithms to analyze relationships between entities and nodes. In addition, graph analytics is empowered by a graph database, contrary to the relational database. A relational database usually has rigid schemas, making it challenging to add new data relations. Graph analytics offers a better choice as this situation can be resolved it using a graph database.
Uses of Graph Analytics
You must be aware of the social media bots that retweet some messages to gather more popularity and traffic. Graph analytics can be used to identify real accounts from fake accounts. It can also be used to prevent virtual frauds and criminal activities executed with the help of online data. Analysts can co-relate information with criminal records and identify relationships and configurations between such activities.
The recommendation engine is one of the essential applications of graph analytics. It runs on the idea of collaborative filtering, which recommends using a specific form of data based on their interest.
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