Ingo Scholtes
University of Zurich
116 Papers
524 Citations
Ingo Scholtes is an academic researcher from University of Zurich. The author has contributed to research in topics: Computer science & Complex network. The author has an hindex of 19, co-authored 109 publications. Previous affiliations of Ingo Scholtes include University of Wuppertal & CERN.
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Papers
From networks to optimal higher-order models of complex systems
TL;DR: Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions, and higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.
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Predicting scientific success based on coauthorship networks
TL;DR: In this paper, the authors who have highly cited papers and those who do not were compared to those who did not, and a machine learning classifier, based only on coauthorship network centrality metrics measured at the time of publication, was able to predict with high precision whether an article will be highly cited five years after publication.
When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks
Ingo Scholtes
- 13 Aug 2017
TL;DR: This work develops a model selection technique to infer the optimal number of layers of such a model and shows that it outperforms baseline Markov order detection techniques and allows to infer graphical models that capture both topological and temporal characteristics of such data.
120
•Posted Content
Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities
TL;DR: In this article, the authors propose an efficient and practical method to identify valid bug reports which refer to an actual software bug, are not duplicates and contain enough information to be processed right away.
103
Categorizing bugs with social networks: a case study on four open source software communities
Marcelo Serrano Zanetti,Ingo Scholtes,Claudio J. Tessone,Frank Schweitzer +3 more
- 18 May 2013
TL;DR: An efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away is proposed.
102