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Found 3 papers, 2 papers with code
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
In particular, to quantify the uncertainties in RCA, we develop a node-level uncertainty quantification algorithm to model the overlapping support regions with high uncertainty; to handle the rarity of minority classes in miscalibration calculation, we generalize the distribution-based calibration metric to the instance level and propose the first individual calibration measurement on graphs named Expected Individual Calibration Error (EICE).
Towards High-Order Complementary Recommendation via Logical Reasoning Network
Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation.
Assigning credit to scientific datasets using article citation networks
We evaluate the quality of DataRank by estimating its accuracy at predicting the usage of real datasets: web visits to GenBank and downloads of Figshare datasets.
