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Personal profile



My main area of research is Network Science, as I work with networks both theoretically as well as practically.  What follows is a brief overview of my research interests, with links to my publications in each area. 

Analysis of small and large networks

  • I run the KONECT project in order to have enough datasets to produce significant research. KONECT is a collection of 230+ networks, and a software library for their generation and analysis.

Spectral and algebraic graph theory

  • I have done much work on representing graphs as matrices and using the decomposition of these matrices to get insights on the graphs. My PhD thesis was about the use of graph matrix decompositions in link prediction. The main result of my PhD thesis is the Spectral Evolution Model.
  • I showed how split-complex numbers can be used to model relationships on dating websites
  • I have contributed to the state of the art in the area of tensor decompositions and joint diagonalisation in network analysis.

Modeling of complex networks

Graphs with special structure

Machine learning in graphs


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Jérôme Kunegis on GitHub

Blog of Jérôme Kunegis

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Research Output

  • 1 Conference contribution

‘Dark Germany’: Hidden patterns of participation in online far-right protests against refugee housing

Schelter, S. & Kunegis, J., 2017, Social Informatics - 9th International Conference, SocInfo 2017, Proceedings. Springer Verlag, Vol. 10539 LNCS. p. 277-288 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10539 LNCS).

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution