Collecting Influencers: A Comparative Study of Online Network Crawlers

Drobyshevskiy, Mikhail; Aivazov, Denis; Турдаков, Денис; Yatskov, Alexander; Varlamov, Maksim; Shayhelislamov, Danil

doi:10.1109/ispras47671.2019.00012

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper [4] proposed a selection algorithm based on the friendship paradox: a random person has fewer friends on average than his friends. Different basic crawlers (RC, MOD, RW, DFS, BFS, DE) are compared in the problem of finding influential vertices [5]. The results of experiments showed that the leading method for a specific graph may change depending on the query budget.…”

Section: Related Workmentioning

confidence: 99%

Graph Neural Network for Crawling Target Nodes in Social Networks

Lukyanov

Drobyshevskiy

Shaikhelislamov

et al. 2022

2022 Ivannikov Ispras Open Conference (ISPRAS)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%