# Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

arXiv: https://arxiv.org/abs/1608.07371 · HTML: https://mlsanity.com/paper/1608.07371/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [PINE: Pipeline for Important Node Exploration in Attributed Networks](https://mlsanity.com/paper/2512.07244/) (2025)
- [TopicTracker: A Platform for Topic Trajectory Identification and Visualisation](https://mlsanity.com/paper/2103.01432/) (2021)

## Abstract (arXiv)

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method overcomes the aforementioned drawbacks defining main paths that are almost 10x less complex while containing more of the relevant important knowledge than the main path networks defined by the existing method.