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On January 14th, Prof. Dr. Ingo Scholtes will give a talk in the AI Talks @ JMU series. The talk with the title "What makes teams successful? Insights from Repository Mining, Network Science, and Empirical Software Engineering" addresses the question how we can use massive data on software repositories to gain insights into collaborative software engineering.

The final report of our Dagstuhl Seminar on "Higher-Order Graph Models: From Theoretical Foundations to Machine Learning" has now been published in the series Dagstuhl Reports of the Leibniz Zentrum für Informatik.

Today, we welcome our new team member Chester Tan, who is joining our chair as a PhD student.

We are delighted to welcome Dr. Anatol Wegner, who has joined our chair as a postdoctoral researcher.

In our recent preprint "Sequential Motifs in Observed Walks" we use higher-order graph models for causal paths to analyze patterns in time series data on networks. The study has been led by collaborators at Northeastern University in Boston, USA.

We wish all of you merry Christmas, happy holidays, and a good start into a successful and - most importantly - healthy new year!

Given the observation of a dynamical process in an an unobserved networked system, what can we say about the underlying interaction topology? Our latest study led by Dr. Unai Alvarez-Rodriguez addresses this important question.

We are happy to announce that our latest study "Big Data = Big Insights? Operationalizing Brooks’ Law in a Massive GitHub Data Set" was accepted for the technical track of the 44th International Conference on Software Engineering (ICSE), to be held in May 2022 in Philadelphia, PA, USA.

Tomorrow, Prof. Scholtes will give an invited talk in the AI Keynote series of the Institute of AI in Management at Ludwig-Maximilians-Universität München.

Next week, Prof. Scholtes will give a talk on Modelling higher-order dependencies in complex systems at the Computational Social Science Seminar held at ETH Zürich.

We currently have multiple open positions for student assistants to support us in the development of the python network analysis package pathpy, e.g. by implementing basic network analysis algorithms, development of unit tests, preparation of tutorials, or completing documentation.