A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector. node2vec is a well-known algorithm to obtain such a graph embedding by sampling neighboring nodes on a given graph with a random walk technique. However, the original node2vec algorithm typically relies on a batch training of graph structures; thus, it is not suited for applications in which the graph structure changes after the deployment. In this paper, we focus on node2vec applications for IoT (Internet of Things) environments. To handle the changes of graph structures after the IoT devices have been deployed in edge environments, in this paper we propose to combine an online sequential training algorithm with node2vec. The proposed sequentially-trainable model is implemented on an FPGA (Field-Programmable Gate Array) device to demonstrate the benefits of our approach. The proposed FPGA implementation achieves up to 205.25 times speedup compared to the original model on ARM Cortex-A53 CPU. Evaluation results using dynamic graphs show that although the accuracy is decreased in the original model, the proposed sequential model can obtain better graph embedding that achieves a higher accuracy even when the graph structure is changed.

翻译：图嵌入是一种新兴方法，可将图结构表示为固定长度的低维向量。node2vec是通过随机游走技术对给定图上相邻节点进行采样以获取此类图嵌入的经典算法。然而，原始node2vec算法通常依赖图结构的批量训练，因此不适用于部署后图结构发生变化的场景。本文聚焦物联网环境中的node2vec应用。为处理边缘环境中物联网设备部署后图结构的动态变化，我们提出将在线序列训练算法与node2vec相结合。该可序列训练模型在现场可编程门阵列器件上实现，以展示本方法的优势。实验表明，与ARM Cortex-A53 CPU上的原始模型相比，所提出的FPGA实现可实现最高205.25倍的加速比。基于动态图的评估结果显示，虽然在原始模型中精度有所下降，但所提出的序列模型即使在图结构变化时仍能获得更优的图嵌入，实现更高精度。