Contains GCN training and a module that generates Erdos Reyni Graphs for training.
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Updated
Aug 16, 2019 - Python
Contains GCN training and a module that generates Erdos Reyni Graphs for training.
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.
Simulation of various complex networks.
Review of different models for generating graphs.
The code and results for finding anchor nodes in different networks which reduce the APL of the network.
Modified gap statistic (gap-com) for regularization selection of sparse networks. This method is aimed for complex network estimation.
Comparing features of the Erdos-Renyi graph with a Real-world graph (MT) with the same number of nodes!
Реализация программы-калькулятора для вычисления характеристик случайных графов // Implementation of program for calculating characteristics of random graphs
Альтернативный экзамен :: Реализация программы-калькулятора для вычисления характеристик случайных графов / Alternative exam :: Implementation of program for calculating characteristics of random 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)
Sample the G(n, m)-model of Erdős–Rényi random graphs.
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