Реализация программы-калькулятора для вычисления характеристик случайных графов // Implementation of program for calculating characteristics of random graphs
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Updated
Jan 10, 2023 - Python
Реализация программы-калькулятора для вычисления характеристик случайных графов // Implementation of program for calculating characteristics of random graphs
Modified gap statistic (gap-com) for regularization selection of sparse networks. This method is aimed for complex network estimation.
Contains GCN training and a module that generates Erdos Reyni Graphs for training.
The code and results for finding anchor nodes in different networks which reduce the APL of the network.
Альтернативный экзамен :: Реализация программы-калькулятора для вычисления характеристик случайных графов / Alternative exam :: Implementation of program for calculating characteristics of random graphs
Comparing features of the Erdos-Renyi graph with a Real-world graph (MT) with the same number of nodes!
Simulation of various complex networks.
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.
Sample the G(n, m)-model of Erdős–Rényi random graphs.
Review of different models for generating graphs.
Simple projects to understand concepts from the Complex Network course at UOC: Structural Descriptors, Models of Complex Networks, Community Detection, Dynamics in CN (Epidemic simulation)
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