论文标题
基于网络的科学概念的链接预测 - 科学4Cast竞赛条目
Network-based link prediction of scientific concepts -- a Science4Cast competition entry
论文作者
论文摘要
我们报告了一个模型，该模型旨在在Science4Cast 2021竞赛的背景下预测复杂的科学概念网络中的链接。我们表明，网络极有利于连接高度的节点，这表明新科学联系主要是在流行概念之间建立的，这构成了我们模型的主要特征。除了这种受欢迎程度的概念外，我们还使用了通过其共同邻居归一化计数量化的节点之间的相似性来改善模型。最后，我们表明，可以通过考虑时间加权的邻接矩阵进一步改进该模型，而较旧和较新的链接在预测中具有更高的影响，分别代表了根源的概念和最先进的研究状态。
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.
