Three-step Algorithms for Detection of High Degree Nodes in Online Social Networks

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

doi:10.1109/ivmem51402.2020.00014

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Other1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They showed experimentally that for large networks the random walk method finds good quality top lists of nodes with high probability. 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].…”

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%