  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. In a traditional approach, the paper
collection is first divided into temporal subsets, and then a co-word network
is independently depicted in a 2D map to characterize each period's trend. To
effectively map emerging research trends from such a time-series of co-word
networks, this paper presents TrendNets, a novel visualization methodology 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. Simulation results on synthetic data demonstrated
that our matrix decomposition approach achieved the best burst detection
performance over four baseline methods. In experiments conducted using papers
published in the past 16 years at three conferences in different fields, we
showed the effectiveness of TrendNets compared to the traditional co-word
representation. We have made our codes available on the Web to encourage
scientific mapping in all research fields.
