OCB contains two graph datasets, Ckt-Bench-101 and Ckt-Bench-301, for representation learning over analog circuits. Ckt-Bench-101 and Ckt-Bench-301 contain graphs (DAGs) that represent analog circuits and provide their corresponding graph-level properties: DC gain (Gain), bandwidth (BW), phase margin (PM),Figure of Tasks: graph-level prediction/regression; analog circuit search (ACS). First open source benchmark for graph learning in analog circuits.
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…It uses monomers as polymer graphs to predict the property of polymer density.
…It uses monomers as polymer graphs to predict the property of polymer melting temperature.
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…It uses monomers as polymer graphs to predict the property of glass transition temperature.
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…It uses monomers as polymer graphs to predict the property of oxygen permeability. It has he limited size (595 polymers), which brings great challenges to the property prediction.
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Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision.