Inheritance patterns in citation networks reveal scientific memes

Tobias Kuhn*, Matjaž Perc, Dirk Helbing

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Original languageEnglish
Article number041036
JournalPhysical Review X
Issue number4
Publication statusPublished - 2014
Externally publishedYes


  • Complex systems
  • Interdisciplinary physics
  • Statistical physics


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