Network-based link prediction of scientific concepts -- a Science4Cast competition entry
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Abstract (arXiv)
We report on a model built to predict links in a complex network of scientific concepts, in the context of the Science4Cast 2021 competition. We show that the network heavily favours linking nodes of high degree, indicating that new scientific connections are primarily made between popular concepts, which constitutes the main feature of our model. Besides this notion of popularity, we use a measure of similarity between nodes quantified by a normalized count of their common neighbours to improve the model. Finally, we show that the model can be further improved by considering a time-weighted adjacency matrix with both older and newer links having higher impact in the predictions, representing rooted concepts and state of the art research, respectively.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 18 Jan 2022.
Builds on
3 of 13 references.
References · 3
- 2014Link Prediction in Social Networks: the State-of-the-Artcited by 22
- 2021Quantum Link Prediction in Complex Networkscited by 5
- 2018Degree distributions of bipartite networks and their projectionscited by 2
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Citing papers per year: 2022: 1.
1 paper.