Fig. 4: Node classification of a citation network.
From: Echo state graph neural networks with analogue random resistive memory arrays
a, An illustration of the large-scale citation network CORA. Each node in the graph is a scholarly article, while an edge indicates a citation between two papers. There are a total of seven article categories, indicated by node colours, according to their discipline. b, The node classification scheme. The input graph is first embedded using the ESGNN according to the protocol shown in Fig. 1f and Methods, followed by a graph convolution layer serving as the readout to produce a classification vector for each node. c, An illustration of simulated node embeddings. Coloured boxes on the left are the zoom-in of node embedding details. d, A node embedding mapped to a 2D spacing using t-SNE, showing clear clustering of nodes of the same categories. e, The accuracy of ten random tests for node classification and the software baseline. The average accuracy is 87.12%, comparable to state-of-the-art algorithms. f, The normalized confusion matrices of the simulated classification results. g, A breakdown of the estimated OPs (red bars) and the associated energy consumption (light-blue bars for a state-of-the-art GPU; dark-blue bars for a projected random resistive memory-based hybrid analogue–digital system). In a forward (backward) pass, the fully trainable model on a state-of-the-art GPU and ESGNN on a projected random resistive memory-based hybrid analogue–digital system consume approximately 24.20 mJ (approximately 63.18 GOPs) and approximately 599.47 μJ (approximately 5.43 GOPs), respectively, revealing a >40.37 fold improvement in the inference energy efficiency (and an approximately 91.40% reduction of the backward pass complexity).
