arXiv:2212.09175
공식 코드 · Papers with Code
Predicting Citi Bike Demand Evolution Using Dynamic Graphs
원문 초록
Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
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Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation
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Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
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RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes
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