piwik-script

Deutsch Intern
Machine Learning for Complex Networks

Publications

We follow an interdisciplinary publication strategy that targets key venues in data science, machine learning, network science, and software engineering as well as journals in fields like information science, statistics, complex systems, or theoretical physics.

Our works have been published in top-tier conferences like SIGKDD, ICSE, WWW, MSR, Graph Drawing, Learning on Graphs, and SIAM Data Mining, as well as in journals like Physical Review Letters, Nature Physics, Nature Communications, Scientometrics, Empirical Software Engineering, and EPJ Data Science.

Below, we only list publications since 2020. Please refer to the profile page of Prof. Scholtes to get a comprehensive overview of past publications of the chair holder.

  • Christoph Gote, Giona Casiraghi, Frank Schweitzer, Ingo Scholtes
    Predicting Variable-Length Paths in Networked Systems using Multi-Order Generative Models
    to appear in Applied Network Science, 2023
  • Vincenzo Perri, Luka Petrovic, Ingo Scholtes
    Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior
    to appear in EPJ Data Science, 2023
  • Christoph Gote, Vincenzo Perri, Christian Zingg, Giona Casiraghi, Carsten Arzig, Alexander von Gernler, Frank Schweitzer, Ingo Scholtes
    Locating Community Smells in Software Development Processes using Higher-Order Network Centralities
    to appear in Social Network Analysis and Mining, 2023
  • Unai Alvarez-Rodriguez, Luka Petrovic, Ingo Scholtes
    Inference of time-ordered multibody interactions
    to appear in Physical Review E, 2023
  • Leonore Röseler, Ingo Scholtes, Christoph Gote
    A Network Perspective on the Influence of Code Review Bots on the Structure of Developer Collaborations
    Registered Report, ICSE CHASE, May 2023

  • F Schweitzer, G Andres, G Casiraghi, C Gote, R Roller, I Scholtes, G Vaccario, C Zingg
    Modeling Social Resilience: Questions, Answers, Open Problems
    Advances in Complex Systems, December 2022
  • Lisi Qarkaxhija, Vincenzo Perri, Ingo Scholtes
    De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs
    In Proceedings of the First Learning on Graphs Conference, PMLR 198:51:1-51:21, December 2022
  • Christopher Blöcker, Jelena Smiljanić, Ingo Scholtes, Martin Rosvall
    Similarity-based Link Prediction from Modular Compression of Network Flows
    In Proceedings of the First Learning on Graphs Conference, PMLR 198:51:1-52:18, December 2022
  • Vincenzo Perri, Lisi Qarkaxhija, Albin Zehe, Andreas Hotho, Ingo Scholtes
    One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium
    In Proceedings of the Third Conference On Computational Humanities Research (CHR2022), December 2022
  • Christoph Gote, Vincenzo Perri, Ingo Scholtes
    Predicting Influential Higher-Order Patterns in Temporal Network Data
    In Proceedings of IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2022), Istanbul, Turkey, November 10-13, 2022, [Best Paper Award]
  • Timothy LaRock, Ingo Scholtes, Tina Eliassi-Rad
    Sequential Motifs in Observed Walks
    In Journal of Complex Networks, Vol. 10, Issue 5, October, 2022
  • Leonore Röseler, Ingo Scholtes, Bernhard Sendhoff, Aniko Hannak
    Willing to revise? Confidence and Recommendation Adoption in AI-Assisted Image Recognition
    In Proceedings of The first International Conference on Hybrid Human-Artificial Intelligence (HHAI2022), Amsterdam, Netherlands, June 2022
  • Luka V Petrović and Ingo Scholtes
    Learning the Markov order of paths in graphs
    In Proceedings of WWW '22: The Web Conference 2022, Lyon, France, April 2022
  • Christoph Gote, Pavlin Mavrodiev, Frank Schweitzer, Ingo Scholtes
    Big Data = Big Insights? Operationalizing Brooks’ Law in a Massive GitHub Data Set
    To appear in Proceedings of the 44th International Conference on Software Engineering (ICSE 2022), Pittsburgh, PA, USA, May 2022

  • Tina Eliassi-Rad, Vito Latora, Martin Rosvall, Ingo Scholtes
    Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)
    Dagstuhl Reports, Vol. 11, No. 7, pp. 139 -- 178, December 2021
    [DOI] [Dagstuhl Research Online Publication Server]
  • Jürgen Hackl, Ingo Scholtes, Luka V Petrović, Vincenzo Perri, Luca Verginer, Christoph Gote
    Analysis and visualisation of time series data on networks with pathpy
    In Proceedings of the 11th Temporal Web Analytics Workshop (TempWeb 2021) in conjunction with The Web Conference 2021, Ljubljana, Slovenia, April 2021
  • Vincenzo Perri and Ingo Scholtes
    Visualisation of Temporal Network Data via Time-Aware Static Representations with HOTVis
    In Proceedings of the 11th Temporal Web Analytics Workshop (TempWeb 2021) in conjunction with The Web Conference 2021, Ljubljana, Slovenia, April 2021
  • Luka Petrović and Ingo Scholtes
    PaCo: Fast Counting of Causal Paths in Temporal Network Data
    In Proceedings of the 11th Temporal Web Analytics Workshop (TempWeb 2021) in conjunction with The Web Conference 2021, Ljubljana, Slovenia, April 2021
    [arXiv 1905.11287]
  • Christoph Gote, Ingo Scholtes and Frank Schweitzer
    Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net
    In Empirical Software Engineering, May 26, 2021
    [arXiv 1911.09484] [SpringerLink]
  • Yan Zhang, Antonios Garas and Ingo Scholtes
    Higher-order models capture changes in controllability of temporal networks
    In Journal of Physics: Complexity, Vol. 2, No. 1, January 29, 2021
    [DOI] [arXiv 1701.06331]