Temporal Analysis of Knowledge Networks

Knowledge network has played an important role in revealing knowledge correlations, exploring innovation trends, and implementing knowledge-guided machine learning. Previous work has studied knowledge network as a static network. However, there is much less study on the evolution of knowledge networks. In this paper, we investigate the evolution of knowledge networks from a temporal network perspective. We extract knowledge networks of different topics from Wikipedia, and examine how local and global properties of these networks evolve over time. We find that many properties such as the power-law exponent of in(out)-degree distribution, density, clustering coefficient, effective diameter, and reciprocity either stay stable or vary little over time after a certain stage. And the shape of macro topology structure of each network is more like a coffee pot rather than a bow-tie. In addition, preferential attachment phenomena are found in the evolution of these knowledge networks. All the code and data are publicly available at https://github.com/XikunHuang/TAKN.