📌 Graph Predictive Systems Summary
Graph predictive systems are tools or models that use the structure of graphs, which are networks of connected points, to make predictions or forecasts. These systems analyse the relationships and connections between items, such as people, places, or things, to predict future events or behaviours. They are often used when data is naturally structured as a network, allowing more accurate insights than treating data points separately.
🙋🏻♂️ Explain Graph Predictive Systems Simply
Imagine a group of friends connected on social media. If you know who talks to whom, you can guess who might become friends next or who might share news quickly. Graph predictive systems work similarly, using connections between items to predict what might happen next in a network.
📅 How Can it be used?
A company can use a graph predictive system to recommend new products to customers based on their social and purchase networks.
🗺️ Real World Examples
A fraud detection team at a bank uses graph predictive systems to monitor transactions. By modelling accounts and transactions as a network, they can predict which accounts are likely to be involved in fraudulent activity based on patterns in the connections.
A logistics company applies graph predictive systems to its delivery routes and hubs, predicting bottlenecks or delays by analysing how different locations and vehicles are connected and interact over time.
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