Explain the principle and sample code of graph neural network

Graph Neural Networks are a deep learning technique that uses multi-layered neural networks to analyze data with complex structures, such as images, speech or text. It converts input data into multi-dimensional data, which is then passed to convolutional layers to build more complex feature representations. Example code: from keras.layers import Conv2Dmodel = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3))) model.add(MaxPooling2D( (2, 2))) model.add(Conv2D(64, (3, 3), activation='relu'))