Net-Ev² proposes a two-stage generative simulator with structure-guided masked pre-training and topology-aware diffusion using graph U-Net down/upsampling to model network event evolution from text inputs, plus a new 6.5M multimodal benchmark and JL-MMD metric.
In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019
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KGEMs for link prediction exhibit high instability in predictions and embeddings from initialization, negative sampling, and other factors, with better MRR not ensuring higher stability.
Introduces Bradley-Terry based ranking of recommender algorithms that varies with dataset statistics, includes a consistency metric, and extends to unseen datasets via BT trees and covariate models.
citing papers explorer
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Net-Ev$^2$: A Generative Simulator for Network Event Evolution
Net-Ev² proposes a two-stage generative simulator with structure-guided masked pre-training and topology-aware diffusion using graph U-Net down/upsampling to model network event evolution from text inputs, plus a new 6.5M multimodal benchmark and JL-MMD metric.
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Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
KGEMs for link prediction exhibit high instability in predictions and embeddings from initialization, negative sampling, and other factors, with better MRR not ensuring higher stability.
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Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Introduces Bradley-Terry based ranking of recommender algorithms that varies with dataset statistics, includes a consistency metric, and extends to unseen datasets via BT trees and covariate models.