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Graph convolutional neural networks with node transition probability-based message passing and DropNode regularization., , , , and . Expert Syst. Appl., (2021)Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization., , , , and . CoRR, (2020)Fake News Detection using Deep Markov Random Fields., , , and . NAACL-HLT (1), page 1391-1400. Association for Computational Linguistics, (2019)Mapping Air Quality in IoT Cities: Cloud Calibration and Air Quality Inference of Sensor Data., , , , , , , and . IEEE SENSORS, page 1-4. IEEE, (2020)Context-Based Analysis of Urban Air Quality Using an Opportunistic Mobile Sensor Network., , , , , , , and . SmartCity360°, volume 323 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 285-300. Springer, (2019)Graph Auto-Encoder for Graph Signal Denoising., , and . ICASSP, page 3322-3326. IEEE, (2020)BRUBIKE: A Dataset of Bicycle Traffic and Weather Conditions for Predicting Cycling Flow., , , and . ISC2, page 432-437. IEEE, (2019)Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference, , , , , , , and . arXiv preprint arXiv:1811.01662, (2018)Context-Aware Deep Markov Random Fields for Fake News Detection., , , , and . IEEE Access, (2021)Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density., , , , , , and . Remote. Sens., 14 (11): 2613 (2022)