Abstract

Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. TrendNets is a novel visualization approach that highlights the rapid changes in edge weights over time. Specifically, we formulated a new convex optimization framework that decomposes the matrix constructed from dynamic co-word networks into a smooth part and a sparse part: the former represents stationary research topics, while the latter corresponds to bursty research topics.

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Reference

If you use our code, please refer to the following paper.

M. Katsurai and S. Ono, “Mapping Emerging Research Trends from Dynamic Co-Word Networks via Sparse Representations,”Scientometrics, vol. 121, no. 3, pp. 1583–1598, 2019.