RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.
Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
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The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
FOCAL fuses unconstrained coverage attention and meta-path anchoring attention to improve multi-label classification on heterogeneous graphs by resolving semantic dilution versus coverage constraint trade-offs.
citing papers explorer
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Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.
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Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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FOCAL-Attention for Heterogeneous Multi-Label Prediction
FOCAL fuses unconstrained coverage attention and meta-path anchoring attention to improve multi-label classification on heterogeneous graphs by resolving semantic dilution versus coverage constraint trade-offs.