# TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation

## TL;DR

TrendNets is a novel visualization method that uses sparse matrix decomposition to identify and highlight emerging research trends and bursts in dynamic co-word networks over time.

## Contribution

It introduces a convex optimization framework for decomposing co-word networks into stationary and bursty topics, improving trend detection in scientific mapping.

## Key findings

- Superior burst detection performance on synthetic data
- Effective identification of emerging trends in real conference data
- Code availability for broader scientific use

## 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. 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.