Time-aware link prediction to explore network effects on temporal knowledge evolution

Quantitative measurements of bibliometrics based on knowledge entities (i.e., keywords) improve competencies in tracking the structure and dynamic development of various scientific domains. Co-word networks (a content analysis technique and type of knowledge network) are often employed to discern relationships among various scientific concepts in scholarly publications to reveal the development and evolution of scientific knowledge. In relation to evolutionary network analysis, different link prediction methods in network science can assist in the prediction of missing links and modelling of network dynamics. These traditional methods (based on topological similarity scores and time series methods of link prediction) can be used to predict future co-occurrence trends among scientific concepts. This study attempted to build supervised learning models for link prediction in co-word networks using network topological similarity metrics and their temporal evolutionary information. In addition to exploring the underlying mechanism of temporal co-word network evolution, classification datasets containing links with both positive and negative labels were also built. A set of topological metrics and their temporal evolutionary information were produced to describe instances of classification datasets. Supervised classifications methods were then applied to classify the links and accurately predict future associations among keywords. Time series based forecasting methods were used to predict the future values of topological evolution. Results in relation to supervised link prediction by different classifiers showed that both static and dynamic information are valuable in predicting new links between literary concepts extracted from scientific literature.