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.
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.