Network-based link prediction of scientific concepts - a Science4Cast competition entry.

被引：1
|
作者：
Moutinho, Joao P. [1 ]
Coutinho, Bruno [1 ]
Buffoni, Lorenzo [1 ]
机构：
[1] Inst Telecomunicacoes, Phys Informat & Quantum Technol Grp, Lisbon, Portugal
来源：
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2021年
基金：
欧盟地平线“2020”;
关键词：
link prediction; complex networks; semantic network;
D O I：
10.1109/BigData52589.2021.9671582
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：5815 / 5819
页数：5
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