From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles

  • Giona CasiraghiEmail author
  • Vahan Nanumyan
  • Ingo Scholtes
  • Frank Schweitzer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. The framework builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.


Statistical analysis Graph theory Network inference Statistical ensemble Relational data Graph mining Graph analysis Network analysis Social network Social network analysis Community structures Data mining Social interactions 



The authors acknowledge support from the Swiss State Secretariat for Education, Research and Innovation (SERI), Grant No. C14.0036, the MTEC Foundation project “The Influence of Interaction Patterns on Success in Socio-Technical Systems”, and EU COST Action TD1210 KNOWeSCAPE. The authors thank Rebekka Burkholz, Giacomo Vaccario, and Simon Schweighofer for helpful discussions.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Chair of Systems DesignETH ZürichZürichSwitzerland

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