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Finding Anomalies in Large Scale Graphs

Finding Anomalies in Large Scale Graphs   Problem definition Given a large, who-calls-whom graph, how can we  nd anomalies and fraud? How can we explain the results of our algorithms? This is exactly the focus of this project. We distinguish two settings: static graphs (no timestamps), and time-evolving graphs (with timestamps for each phone). We further subdivide into two sub-cases each: supervised, and unsupervised. In the supervised case, we have the labels for some of the nodes (‘fraud’/’honest’), while in the unsupervised one, we have no labels at all. Major lessons For the supervised case, the natural assumption is that […]

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