Network-based link prediction of scientific concepts -- a Science4Cast competition entry
This work addresses link prediction in scientific networks, which is incremental as it builds on existing network-based methods without introducing a new paradigm.
The authors tackled the problem of predicting links in a scientific concept network for the Science4Cast 2021 competition, achieving results by leveraging node popularity, similarity via common neighbors, and time-weighted adjacency matrices, though no concrete numbers are provided.
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.