📌 Graph Predictive Analytics Summary
Graph predictive analytics is a method that uses networks of connected data, called graphs, to forecast future outcomes or trends. It examines how entities are linked and uses those relationships to make predictions, such as identifying potential risks or recommending products. This approach is often used when relationships between items, people, or events provide valuable information that traditional analysis might miss.
🙋🏻♂️ Explain Graph Predictive Analytics Simply
Imagine a group of friends where everyone knows each other in different ways. If you want to guess who might become friends next, you would look at who already knows each other and their connections. Graph predictive analytics works in a similar way, using information about how things are linked to predict what will happen next.
📅 How Can it be used?
A company could use graph predictive analytics to predict which customers are likely to buy a new product based on their social network connections.
🗺️ Real World Examples
A bank uses graph predictive analytics to detect fraud by analysing how accounts are connected through transactions. If a suspicious pattern of transfers emerges between accounts that are not usually linked, the system can flag this for further investigation, helping prevent financial losses.
A streaming service applies graph predictive analytics to recommend shows by examining viewing patterns and social connections between users. If two friends have similar tastes and one watches a new series, the system suggests it to the other, improving user satisfaction.
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