图卷积神经网络综述OA北大核心CSTPCD

Graph convolutional neural network(GCN)has emerged as the intersection of graph theory and deep learning,becoming the hotspot research field of machine learning.Therefore,a comprehensive overview of the GCN is provided,and the available studies of GCN into two typical categories are summarized:spectral-based methods and spatial-based methods.These two typical types of GCN models are extensively discussed,the fundamental theoretical underpinnings of the graph convolution operations are delved into,diverse applications of GCN across various domains are showcased,the major challenges encountered by GCN are summarized,and valuable insights into the future trends of GCN advancement are offerred.Additionally,the potential utilization of GCN in butterfly recognition tasks is investigated,particularly in identifying butterfly species by leveraging images captured in natural habitats.