A novel DFS/BFS approach towards link prediction
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...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2409.11687 |