A novel DFS/BFS approach towards link prediction
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909319032733696 |
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| author | Dörpinghaus, Jens Hübenthal, Tobias Stepanov, Denis |
| author_facet | Dörpinghaus, Jens Hübenthal, Tobias Stepanov, Denis |
| contents | Knowledge graphs have been shown to play a significant role in current knowledge mining fields, including life sciences, bioinformatics, computational social sciences, and social network analysis. The problem of link prediction bears many applications and has been extensively studied. However, most methods are restricted to dimension reduction, probabilistic model, or similarity-based approaches and are inherently biased. In this paper, we provide a definition of graph prediction for link prediction and outline related work to support our novel approach, which integrates centrality measures with classical machine learning methods. We examine our experimental results in detail and identify areas for potential further research. Our method shows promise, particularly when utilizing randomly selected nodes and degree centrality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A novel DFS/BFS approach towards link prediction Dörpinghaus, Jens Hübenthal, Tobias Stepanov, Denis Social and Information Networks Data Structures and Algorithms Knowledge graphs have been shown to play a significant role in current knowledge mining fields, including life sciences, bioinformatics, computational social sciences, and social network analysis. The problem of link prediction bears many applications and has been extensively studied. However, most methods are restricted to dimension reduction, probabilistic model, or similarity-based approaches and are inherently biased. In this paper, we provide a definition of graph prediction for link prediction and outline related work to support our novel approach, which integrates centrality measures with classical machine learning methods. We examine our experimental results in detail and identify areas for potential further research. Our method shows promise, particularly when utilizing randomly selected nodes and degree centrality. |
| title | A novel DFS/BFS approach towards link prediction |
| topic | Social and Information Networks Data Structures and Algorithms |
| url | https://arxiv.org/abs/2409.11687 |