Graph identification refers to methods that transform observational data described as a noisy, input graph into an inferred "clean" output graph. Examples include inferring social networks from communication data, identifying gene regulatory networks from protein-protein interactions, etc. On the flip-side, there is a growing interest in anonymizing social network data, and understanding the different types of privacy threats inherent in relational data. In this talk, I will discuss some of the key processes involved in identification (entity resolution, link prediction, collective classification and group detection) and I will overview results showing that on several well-known social media sites, we can easily and accurately recover information that users may wish to remain private.
Speaker: Lise Getoor
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