Data and code used to generate results for the paper "The effect of heterogeneity on hypergraph contagion models"

• simplexContagion.py: Implements the functions required to run the microscopic Markov model on a hypergraph
  • microscopicSimplexSISDynamics(): Implementation of a microscopic Markov model including 3-hyperedges. Outputs a population average time-series.
  • generateSISEquilibriaParallelized(): Runs several different equilibrium curves for each specified value of $\beta_3$ in parallel. I.e. it runs runOneCurve() for each $\beta_3$ value.
  • runOneCurve(): Runs a single equilibrium curve.
  • The rest of this file was not used in the paper
• simplexUtilities.py: Implements functions used to generate the adjacency matrix and the lists of hyperedges.
  • criticalBeta3(): Computes $$\beta_3^c$$ from the network properties (Methods in Section IV)
  • invCDFPowerLaw() and generatePowerLawDegreeSequence(): generate a power-law degree sequence.
  • generateUniformDegreeSequence(): Generates a uniformly distributed degree sequence.
  • generateConfigModelAdjacency(): Generates an adjacency matrix from a degree sequence using the configuration model
  • generateConfigModelSimplexList(): Generates a list of hyperedges using the configuration model
  • generateUniformSimplexList(): Generates a list of hyperedges that a uniformly distributed on the nodes
  • The rest of this file is not used in the paper
• simplexVisualize.py: Implements different functions for visualizing the mean field curves and bistability index
• simplexTheory.py: Implements functions dealing with the mean-field model
  • getPhase(): Gets the number of roots solving the mean-field equations
  • solveEquilibrium(): Solves the mean-field equation for given degree distribution, $\gamma$, $\beta_2$, and $\beta_3$
  • generateTheoreticalDegreeHist(): generates a degree list with probabilities from a given distribution
  • calculateTheoreticalCriticalAlpha(): Finds $\beta_3^c$ using methods described in Appendix A.3
  • The rest of this file were auxiliary functions or not used.

• runModelInParallel.py: Runs the microscopic model in parallel
• outputPhasePlotBetaAlpha.py: Generates the phase plots for the mean-field models.
• outputRatioForKMaxVsRInParallel: Generates the contour plots of $\beta_3^c/\beta_2^c$ for $k_{max}$ vs. $r$.

Functions and data used to plot the figures in the paper