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When Riders Become Nodes: Mapping Fraud in Ride-Hailing with Graph Neural Networks

A ride can look perfectly normal. The driver accepts a request, reaches the pickup point, and ends the trip shortly afterward. Nothing in that single transaction necessarily screams fraud. But place it beside the driver’s repeated early completions, the passengers who frequently disappear from the platform after pickup, and the same locations where similar cancellations occur, and the pattern changes. ...

January 4, 2026 · 17 min · Zelina
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Trade Winds and Neural Currents: Predicting the Global Food Network with Dynamic Graphs

TL;DR for operators A recent arXiv paper proposes IVGAE-TAMA-BO, a dynamic graph model for predicting whether future crop-trade links are likely to exist in the global food network.1 That sounds grand. The useful version is narrower and better: the model tries to learn how trade relationships rewire over time, especially when old routes persist, weak links disappear, and new pairings emerge. ...

November 6, 2025 · 14 min · Zelina