There are many graph properties. but here we provided a library in C++ that provides a few properties of graphs.

Generate trust graphs for networks like Stellar.

An efficient generator of functional digraphs up to isomorphism

A minimalistic python package to generate a k-colorable graph.

Random connected graph generation

Semantic model of LMM analysis

A PHY180 Project, LaTeX Report Paper titled "Analysis on the Period vs. Angle, Period vs. Length & Q Factor (Damping Factor) of Pendulum"

Faci: a Java analyzer for Facebook Cliques Interactions

C++ library for graph drawing and network analysis

TNO PET Lab - Synthetic Data Generation (SDG) - Graph - Generation - GraphBin

Python tool for generating undirected weighted graphs representing road networks.

SUTD Class of 2022 Teammate Retention Rate Study

The Preferential Deletion Model with Changing in Existing Connections (PDCModel) is an extension of the discrete-time random graph generation process described by Narsingh Deo and Aurel Cami in 2005. This new model accounts for changes in existing edges for every unit of time, representing the behavior of social circles more accurately. 2020.

Code for "Adversarially Generating Graphs of Bounded Rank" published at IEEE DSAA'21

[KDD MLG'20] Class-Assortative Barabasi Albert Model for Graph Generation

The Preferential Deletion Model (PDModel) is an implementation of the original discrete-time random graph generation process described by Narsingh Deo and Aurel Cami. 2020.

Understanding Evolution Through Spatio-Temporal Graph Generation

A Temporal Networks Library written in Python

Learning-guided Graph Dual Adversarial Domain Alignment (LG-DADA) framework for predicting a target graph from a source graph.