NCPNet applies non-exchangeable conformal prediction to temporal graphs by diffusing non-conformity scores over graph and time neighbors and learning weighted quantiles to reduce prediction set size.
Developing a Temporal Bibliographic Data Set for Entity Resolution
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abstract
Entity resolution is the process of identifying groups of records within or across data sets where each group represents a real-world entity. Novel techniques that consider temporal features to improve the quality of entity resolution have recently attracted significant attention. However, there are currently no large data sets available that contain both temporal information as well as ground truth information to evaluate the quality of temporal entity resolution approaches. In this paper, we describe the preparation of a temporal data set based on author profiles extracted from the Digital Bibliography and Library Project (DBLP). We completed missing links between publications and author profiles in the DBLP data set using the DBLP public API. We then used the Microsoft Academic Graph (MAG) to link temporal affiliation information for DBLP authors. We selected around 80K (1%) of author profiles that cover 2 million (50%) publications using information in DBLP such as alternative author names and personal web profile to improve the reliability of the resulting ground truth, while at the same time keeping the data set challenging for temporal entity resolution research.
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Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks
NCPNet applies non-exchangeable conformal prediction to temporal graphs by diffusing non-conformity scores over graph and time neighbors and learning weighted quantiles to reduce prediction set size.