Introduction to multi-edge network inference in R using the ghypernet-package

European Symposium on Societal Challenges in Computational Social Science - 2019 - EuroCSS

Giona Casiraghi, ETH Zurich and Laurence Brandenberger, ETH Zurich

September 2, 2019: Half-day workshop, Morning session 

The half-day workshop provides an introductory tutorial on Network Regression Models (NRMs) for multi-edge networks. Network models are the most important tools in network science for the analysis of complex systems.

Currently, the estimation of models for large systems is hampered by the computational burden posed by numerical simulations on which most models rely. For this reason, analytical models and models that do not rely on simulations for the estimation of their parameters are the optimal approach to deal with large-scale complex systems.

In this workshop, we present a new network inference model based on generalised hypergeometric ensembles. These are a recently developed class of analytically tractable ensembles for multi-edge networks. They contain random graphs generated by fixing degree sequences, and incorporating arbitrary propensities of nodes pairs to be connected. NRMs allow to estimate the effect size and significance as predictors in a regression of known relations between nodes. This is achieved by incorporating such relations in the ensemble, in an attempt to model the original data.  As the model does not rely on numerical simulations, it is easy to apply, fast and well-suited for large-scale networks.


Register for the workshop here.


Prerequisites: All analyses are performed in R using the R-package 'ghypernet'. Participants should be familiar with base-R commands as well as basic network concepts.

Event format: The workshop is split into three parts: After an introduction to hypergeometric ensembles, we demonstrate some empirical applications. We then provide an extensive lab session where participants are will get a chance to test the model hands-on (either with their own data or some example data provided by us).



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