Exploring Complex Networks Dynamics

Prof. Dr. Verena Wolf
Gerrit Großmann, MSc
Michael Backenköhler, MSc

Please contact Gerrit Großmann for questions about the seminar.


Please use the seminar assignment system to register.


Many real-world phenomena like rumor spreading, contagions in financial markets, epidemic outbreaks, or cognitive processes can be expressed in the terms of complex networks. The seminar explores dynamical processes which are linked to networked structures in intriguing ways.  In particular, we aim at understanding the complex interplay between the topology and the emerging dynamics of a network.  We analyze brain networks or online social networks and explore various techniques for their analysis and classification ranging from diffusion models and stochastic simulations to deep learning approaches.


  • Where? All sessions will take place in room 328 (E 1.3).
  • A Kick-off meeting will take place on the Friday, 18th October, 12:30.
  • The seminar takes place Thursdays 10:15-11:45 with two presentations per session (2 x 20-30 minutes + discussion).
  • We meet at 14.11.19, 28.11.19, 19.12.19, 9.01.20, 23.01.20, 06.02.20.
  • The seminar language is English.
  • The seminar earns you 7 ECTS.


There are no prerequisites to take the seminar but a basic understanding of linear algebra and probability theory will be useful.


The final grade consists of: your presentation (40%), reports (40%), and participation during the seminar discussions (20%).


Identify the key ideas and concepts and give a self-consistent presentation explaining these concepts to your fellow students. Assume no prior knowledge about the topic. Read related work and include it where it seems useful to you or where it is necessary to understand the topic. Meet with your supervisor and send her your slides at least one week before your presentation (deadline is Wednesdays 23:59). The duration should be 20-30 min.


In each seminar session (including the session of your own presentation), choose one of the two presented papers/topics and write a short report (600-1600 words) about it. Shortly summarize (what you consider to be) the main contribution or most intriguing idea of the paper. Explain what did you like/dislike about the paper (both methodically and presentation-wise). Highlight connections to other seminar papers. We do not expect comprehensive understanding of the paper.

You can use the Springer LNCS Latex template (every other reasonable formatting is fine too).

Send the reports as a .pdf-files to Gerrit Großmann by Email within the following 13 days of the presentation. The deadline is always Wednesdays 23:59. The subject should be NetworkSeminarSubmission+<YourFullName> (without < and >). The corresponding filename should be be <YourFullName>_<TopicName>.pdf (without < and >).

Each participant gets one session where he/she can skip the report. Since we have six sessions, we expect each participant to submit five short reports (but maximally one repert per session).


Read each presented paper in advance (of course, we do not expect you to understand them fully) and actively participate in the discussion. We expect each participant to attend each session.


Session Date Student Topic
1 14.11.19 Lukas Wachter Cascading Effects of Targeted Attacks on the Power Grid
1 14.11.19 Redion Xhepa Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
2 28.11.19 Christina Eimer Network control principles predict neuron function in the Caenorhabditis elegans connectome
2 28.11.19 Julian Zimmerlin BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
3 19.12.19 Timon Ulrich Structural Deep Brain Network Mining (Report deadline: 08.01.20)
3 19.12.19 Lisa Heidmann The epidemic spreading model and the direction of information flow in brain networks (Report deadline: 08.01.20)
4 9.01.20 Sabrina Lauer Reconstructing an Epidemic Over Time
4 9.01.20 Bastian Herra Spy vs. Spy: Rumor Source Obfuscation
5 23.01.20 Adrian Dapprich Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks (also consider DeBot: Twitter Bot Detection via Warped Correlation)
5 23.01.20 Dynamic Network Model from Partial Observations
6 06.02.20 Nicolas Faroß Spectral Measures of Distortion for Change Detection in Dynamic Graphs
6 06.02.20 Saleh Al Mohamad Diffusion-Convolutional Neural Networks