Preprint
The evolution of digital technologies: a network perspective on machine learning
- Abstract:
- The development of digital technologies such as Machine Learning can be described empirically as a co-evolving network based on online platform data. Here, we construct a network of technologies related to machine learning based on data from Stack Overflow, the world’s largest question-and-answer website for programming questions.1 This network reveals the changing centrality of machine learning topics, libraries, and related programming languages over time as the network links rewire when novel technologies are introduced. It thus allows for understanding the development of the field as combinatorial technological evolution, shaped by the replacement of older technologies by novel ones. The data can be used to test network models on innovation and novelty, and on creative destruction.
- Publication status:
- Published
- Peer review status:
- Not peer reviewed
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Access Document
- Files:
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(Preview, Pre-print, pdf, 171.7KB, Terms of use)
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- Preprint server copy:
- 10.31235/osf.io/cnq6p
Authors
- Preprint server:
- OSF
- Publication date:
- 2019-10-09
- DOI:
- Server owner:
- Center for Open Science
- Language:
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English
- Pubs id:
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1064090
- UUID:
-
uuid_b8d32c32-ec8a-462c-acfc-7e2a1a0bcbe9
- Local pid:
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pubs:1064090
- Source identifiers:
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W4249937866
- Deposit date:
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2026-01-10
- ARK identifier:
Terms of use
- Copyright holder:
- Fabian Braesemann
- Copyright date:
- 2019
- Rights statement:
- ©2019 The Authors. This paper is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
- Licence:
- CC Attribution (CC BY)
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