Clique on the network name to see detailed description, code, Betti curves, and graph statistics. All models are included in the zip file here

## Edge-weighted network models

Description: Nodes are points chosen uniformly from the unit cube with edge weights inversely proportionate to distance.Reference: Kahle, 2009 |

**Node-ordered network models**

## Affinity Model

Description: Each node assigned an affinity for future edges. New nodes connect to old nodes based on old node edge affinity.Reference: Sizemore et al., 2017 |

Description: At each step a node is added with links attaching to each of the existing nodes with probability p = c, a constant.Reference: Sizemore et al., 2017 |

Modular

Description: Creates a modular binary graph with pre-defined community size. Nodes randomly assigned to communities and preferentially attach to community members.Reference: Sizemore et al., 2017 |

Description: At each node addition the probability of edges forming between the new node and existing nodes is p = alpha*|sin(k*pi*n/nNodes)| with alpha a scaling factor and k determining the number of periods.Reference: Sizemore et al., 2017 |

**Proportional Probability**

Description: At each step the links from the new node to existing nodes exist with probability p = n/nNodes, with nNodes the final number of nodes.Reference: Sizemore et al., 2017 |