Cheng Li, M.Sc.

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.
• https://doi.org/10.1038/s41467-021-27089-8

Discussion Papers

Rose, Michael; Ghosh, Mainak; Erhardt, Sebastian; Li, Cheng; Buunk, Erik; Harhoff, Dietmar (2025). Tracing the Flow of Knowledge From Science to Technology Using Deep Learning, arXiv:2512.24259.

• We develop a language similarity model suitable for working with patents and scientific publications at the same time. In a horse race-style evaluation, we subject eight language (similarity) models to predict credible Patent-Paper Citations. We find that our Pat-SPECTER model performs best, which is the SPECTER2 model fine-tuned on patents. In two real-world scenarios (separating patent-paper-pairs and predicting patent-paper-pairs) we demonstrate the capabilities of the Pat-SPECTER. We finally test the hypothesis that US patents cite papers that are semantically less similar than in other large jurisdictions, which we posit is because of the duty of candor. The model is open for the academic community and practitioners alike.