Cheng Li, M.Sc.

Doctoral Student and Junior Research Fellow

Innovation and Entrepreneurship Research

+49 89 24246-453

Areas of Interest:

Innovation Diffusion, Innovation Strategy, Intellectual Property Rights, Complex Networks, Artificial Intelligence

Academic Résumé

Since 06/2023
Junior Research Fellow and Doctoral Candidate at the Max Planck Institute for Innovation and Competition
(Innovation and Entrepreneurship Research)

2018 – 2021
Master of Science, Robotics, Systems and Control
Thesis: Data-Driven Analysis of the “Ranking Quality” Network Formation Model
ETH Zurich, Zurich, Switzerland

2015 – 2018
Bachelor of Science, Mechanical Engineering
Technische Universität Dresden

Work Experience

01/2022 – 05/2023
Patent Engineer
Wuesthoff & Wuesthoff, Munich

07/2020 – 02/2021
Student Assistant
ETH Zurich, Switzerland

09/2019 – 02/2020
Research Intern
Huawei, Zurich, Switzerland

09/2016 – 01/2018
Teaching Assistant
TU Dresden

Honors, Scholarships, Academic Prizes

DAAD STIBET Graduation Scholarship


Articles in Refereed Journals

Pagan, Nicolò; Mei, Wenjun; Li, Cheng; Dörfler, Florian (2021). A Meritocratic Network Formation Model for the Rise of Social Media Influencers, Nature Communications 2021.

  • Many of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.