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

Graph Neural Network for Crawling Target Nodes in Social Networks

Graph Neural Networks share with Logic Programming several key relational
inference mechanisms. The datasets on which they are trained and evaluated can
be seen as database facts containing ground terms. This makes possible modeling
their inference mechanisms with equivalent logic programs, to better understand
not just how they propagate information between the entities involved in the
machine learning process but also to infer limits on what can be learned from a
given dataset and how well that might generalize to unseen test data.
This leads us to the key idea of this paper: modeling with the help of a
logic program the information flows involved in learning to infer from the link
structure of a graph and the information content of its nodes properties of new
nodes, given their known connections to nodes with possibly similar properties.
The problem is known as graph node property prediction and our approach will
consist in emulating with help of a Prolog program the key information
propagation steps of a Graph Neural Network's training and inference stages.
We test our a approach on the ogbn-arxiv node property inference benchmark.
To infer class labels for nodes representing papers in a citation network, we
distill the dependency trees of the text associated to each node into directed
acyclic graphs that we encode as ground Prolog terms. Together with the set of
their references to other papers, they become facts in a database on which we
reason with help of a Prolog program that mimics the information propagation in
graph neural networks predicting node properties. In the process, we invent
ground term similarity relations that help infer labels in the test set by
propagating node properties from similar nodes in the training set and we
evaluate their effectiveness in comparison with that of the graph's link
structure. Finally, we implement explanation generators that unveil performance
upper bounds inherent to the dataset.
As a practical outcome, we obtain a logic program, that, when seen as machine
learning algorithm, performs close to the state of the art on the node property
prediction benchmark.

本文旨在探究利用逻辑程序模拟图神经网络的信息传播机制，在解决节点属性预测问题中的应用，通过实现一个类似 Prolog 程序的逻辑程序来模拟图神经网络的信息传播阶段，并利用相似节点的属性推断测试集中的节点属性，与链接结构进行比较，得到更好的表现，在 ogbn-arxiv 节点属性推断基准测试上实现了与领先水平接近的结果，并为该数据集的性能上限提供了解释。