A rule-based strikingness measure is added to TKGR metrics to weight rare events higher, revealing that models weaken on striking events and ensemble gains come mostly from trivial fits.
InKDD ’21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021, Feida Zhu, Beng Chin Ooi, and Chunyan Miao (Eds.)
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IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
FedEPD decouples topological purification from semantic recalibration using energy-guided pruning and prototype injection to improve minority performance in federated long-tailed graph learning.
citing papers explorer
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Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning
A rule-based strikingness measure is added to TKGR metrics to weight rare events higher, revealing that models weaken on striking events and ensemble gains come mostly from trivial fits.
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Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
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Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
FedEPD decouples topological purification from semantic recalibration using energy-guided pruning and prototype injection to improve minority performance in federated long-tailed graph learning.