Graphs and more Complex structures for Learning and Reasoning (GCLR) workshop was held at AAAI 2021. For more details about the workshop, please visit website: https://sites.google.com/view/gclr2021/.
Speaker's Bio: Prof. Gesine Reinert is a University Professor in Statistics, a Fellow of Keble College, and also a Fellow of the Alan Turing Institute. The general area of her research includes Applied Probability and many of the problems from the area of Computational Biology. More specifically, she is interested in statistics to investigate networks in a statistically rigorous fashion. In this talk, she will talk about her recent work on anomaly detection in networks using spectral methods and network comparison approaches.
Title of the talk: Anomaly detection in networks
Abstract: Detecting fraud is a global challenge. This talk will mainly focus on financial and infrastructure transaction networks. There are many methods available to detect specific anomalies; this talk will present an approach for detecting unknown anomalies. To that purpose a strategy is used with derives features from network comparison methods and spectral analysis, and then a random forest method is applied to classify nodes as normal or anomalous. The method is tested on synthetic data as well as infrastructure data.
This talk is based on joint work with Andrew Elliott, Mihai Cucuringu, Milton Martinez Luaces and Paul Reidy.