Revealing citation cartels in network data

Developing methods to detect citation communities and analysing academic citation network data

Project status



Bibliographic assessment is not a straightforward exercise. One type of manipulative behaviour is the practice of self-citation. Beyond self-citation, frequent mutual citations between two journals or researchers are the simplest form of 'citation stacking'. This project will seek to quantify 'citation cartel' behaviour in academic citation networks.

Explaining the science

Networks are a useful language for untangling complex systems. Modern network science effectively started in 1998-99 and its ongoing success is an interdisciplinary effort, involving computer science, statistical physics and applied mathematics and various other fields.

This project will use and develop methods related to community detection in networks to develop algorithms, which will then be applied to citation data.

Project aims

The project has three main aims:

  • Develop methods to detect citation communities.
  • Quantify the impact of detected communities.
  • Analysis of academic citation networks.


With criticisms being included, publication records are widely used to evaluate researchers in hiring and promotion decisions, internal staff reviews and screening of grant proposals. Outcomes of this project may help assessors have new understanding of citation data. Potential beneficiaries also include Higher Education Funding Council for England (HEFCE) and bodies that produce university league tables. 

Outcomes of this project can be also applied to citation networks of journals. Researchers and enterprises working on journal ranking using citation network data and owners of citation data are also potential beneficiaries of the project.