📌 Graph Predictive Modeling Summary
Graph predictive modelling is a type of data analysis that uses the connections or relationships between items to make predictions about future events or unknown information. It works by representing data as a network or graph, where items are shown as points and their relationships as lines connecting them. This approach is especially useful when the relationships between data points are as important as the data points themselves, such as in social networks or transport systems.
🙋🏻♂️ Explain Graph Predictive Modeling Simply
Imagine your group of friends as a web, where each person is a dot and every friendship is a line connecting two dots. Graph predictive modelling is like guessing who might become friends next based on the current connections in the group. By looking at how everyone is linked, you can make smart guesses about who is likely to connect in the future.
📅 How Can it be used?
Predict which users in a social app are likely to connect based on their shared friends and interactions.
🗺️ Real World Examples
A streaming service uses graph predictive modelling to recommend new shows to users. By analysing which users have watched similar programmes and how they are connected through shared viewing habits, the system predicts what each person might enjoy next.
A financial institution uses graph predictive modelling to detect fraudulent transactions. By mapping out transaction flows between accounts, the model predicts suspicious patterns and flags potential fraud based on unusual connections.
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