This repository hosts the lab exercises for the course Social Networks Analysis of NTUA ECE.
The goal of this lab was to create complex topologies using the networkx library and study them using multiple metrics. One of the metrics we use is the ego-betweenness centrality for which we implement a function to calculate it for each node of a graph using the algorithm described in the paper Ego network betweenness by Martin Everett and Stephen P.Borgatti.
The goal of this lab was to analyze both artificial and real topologies using the metrics from Lab 1 and some additional metrics in order to study the social structure of those networks. In addition, we performed community detection using three different algorithmic techniques in order to compare their performances.
The goal of this lab was to develop intuition about the basic features of a genetic algorithm. In order to do so, we implemented two genetic algorithms. The first one provides a solution to the ONEMAX toy problem. The second is an implementation of the GA-Net for community detection in graphs.