Graph Neural Network for Crawling Target Nodes in Social Networks
Kirill Lukyanov Mikhail Drobyshevskiy Danil Shaikhelislamov Denis Turdakov
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.