Abstract: An architecture search method and an architecture search apparatus for a large-scale graph, and a device and a storage medium are provided. The method includes: obtaining a subgraph of a large-scale graph by performing local sampling on the large-scale graph; sampling a plurality of neural network architectures in a pre-constructed super network according to a pre-customized importance sampling strategy; obtaining a plurality of trained neural network architectures by training, according to a peer learning method, the plurality of neural network architectures with the subgraph; obtaining a trained super network by iteratively executing the subgraph sampling, the architecture sampling, and the architecture training; and selecting an optimal architecture in the trained super network to process the large-scale graph to obtain a graph processing result.

Abstract: The present disclosure provides a composite porous separator and an electrochemical device. The composite porous separator comprises: a composite porous substrate; and a composite porous coating coated on at least one surface of the composite porous substrate. The composite porous substrate comprises a filler A and a polymer matrix, the filler A is at least one selected from a group consisting of inorganic particles and organic particles; the composite porous coating comprises a filler B and an adhesive, the filler B is at least one selected from a group consisting of inorganic particles and organic particles. The electrochemical device has the above composite porous separator. The present disclosure improves the thermal stability of the composite porous separator, and improves the anti-deformation capability and the capacity retention rate of the electrochemical device, and further improves the cycle performance and the low temperature dynamic performance of the electrochemical device.