Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper we adopt graph neural networks for this purpose and show they are competitive to traditional classifiers and are better for individual cases. Additionally we suggest a training sample boosting technique, which helps to diversify the training set at early stages of crawling and thus improves the predictor quality. The experimental study on three types of target set topology indicates GNN based approach has a potential in crawling task, especially in the case of distributed target nodes.

社交网络抓取是近年来活跃研究的焦点。本文采用图神经网络来预测未知图中的目标节点属性，并证明其竞争力超过传统分类器且在个别情况下表现更好。此外，我们提出了一种训练样本增强技术，在抓取的早期阶段帮助多样化训练集，并提高预测器的质量。针对三种目标集拓扑结构的实验研究表明，基于图神经网络的方法在抓取任务中具有潜力，特别是在分布式目标节点的情况下。

社交网络中的目标节点爬行的图神经网络