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GEDet: Adversarially Learned Few-shot Detection of Erroneous Nodes in Graphs.

, , , and . IEEE BigData, page 44-53. IEEE, (2020)

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Fair Group Summarization with Graph Patterns., , , , and . ICDE, page 1982-1994. IEEE, (2023)Explaining Missing Data in Graphs: A Constraint-based Approach., , , and . ICDE, page 1476-1487. IEEE, (2021)GRIP: Constraint-based Explanation of Missing Answers for Graph Queries., , , and . SIGMOD Conference, page 2779-2783. ACM, (2021)GEDet: Adversarially Learned Few-shot Detection of Erroneous Nodes in Graphs., , , and . IEEE BigData, page 44-53. IEEE, (2020)ModsNet: Performance-aware Top-k Model Search using Exemplar Datasets., , , , , , , and . Proc. VLDB Endow., 17 (12): 4457-4460 (August 2024)Selecting Top-k Data Science Models by Example Dataset., , , , , , , and . CIKM, page 2686-2695. ACM, (2023)GALE: Active Adversarial Learning for Erroneous Node Detection in Graphs., , , and . ICDE, page 1705-1718. IEEE, (2023)Diversified Subgraph Query Generation with Group Fairness., , , and . WSDM, page 686-694. ACM, (2022)An Efficient Ride-Sharing Framework for Maximizing Shared Routes., , , , , and . ICDE, page 1795-1796. IEEE Computer Society, (2018)CRUX: Crowdsourced Materials Science Resource and Workflow Exploration., , , , , , and . CIKM, page 5014-5018. ACM, (2022)