Graph Neural Network for Crawling Target Nodes in Social Networks
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911806907219968 |
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| author | Lukyanov, Kirill Drobyshevskiy, Mikhail Shaikhelislamov, Danil Turdakov, Denis |
| author_facet | Lukyanov, Kirill Drobyshevskiy, Mikhail Shaikhelislamov, Danil Turdakov, Denis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network for Crawling Target Nodes in Social Networks Lukyanov, Kirill Drobyshevskiy, Mikhail Shaikhelislamov, Danil Turdakov, Denis Social and Information Networks Machine Learning I.2.6 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. |
| title | Graph Neural Network for Crawling Target Nodes in Social Networks |
| topic | Social and Information Networks Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2403.13865 |