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From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets).
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1. 7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption.
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