Julia and Python complex system applications in ecology, epidemiology, sociology, economics & finance; network science models including Bianconi-Barabási, Barabási-Albert, Watts-Strogatz, Waxman Model & Erdős-Rényi; graph theory algorithms involving Gillespie, Bron Kerbosch, Ramsey, Bellman Ford, A*, Kruskal, Borůvka, Prim, Dijkstra, DSatur, Ran…

The algorithm based on the UBQP model (Aref et al. 2018) for computing the exact value of frustration index (also called line index of balance)

A Python module for generating random network flows problem instances in DIMACS graph format.

The algorithm based on the XOR model (Aref et al. 2019) for computing the exact value of frustration index (also called line index of balance)

SJTU-CS499-Mathematical-Foundations-of-Computer-Science

Started as a numerical task to prove convergence of number of triangles in ER graph, this project grown to consist of various random graph models' implementations, such as Erdos-Renyi random graph, Generalized random graph, Configuration Model.

Work on INFOF-308 course at ULB

Generate, color, and visualize random graphs using Python's NetworkX and Matplotlib. Includes compression and storage of graph data with .gz and pickle. Ideal for exploring graph coloring and greedy algorithms in graph theory.

Bootstrap-based inference for eigenvalues of weighted networks. Statistical inference for eigenvalues in weighted network data using bootstrap methods

A project that involves generating random graphs and looking into their properties.