This repo contains the python notebooks for the lab exercises of the course "Social Network Analysis", which is part of the ECE-NTUA undergraduate program (9nth semester).
Course Description
Introduction to Network Science: Basic definitions of networks, role of networks and examples in different applications, topology control and network creation. Elements of graph theory and overview of basic definitions. Structure and characteristics of complex and social networks: random network models, small-world networks, power-law networks, scale-free networks, regular networks, random geometric networks (random geometric graphs), etc. Analysis elements of complex and social networks: analysis metrics (node degree distribution, aggregation coefficient, network centrality, etc.), selective connection and network creation/evolution. Evolutionary computing: genetic algorithms, diagnostic algorithms, parallel computing and heuristic computing methods. Applications in Telecommunications and Computer Science: topology control, routing and resource assignment, effect of network structure on information dissemination/opinion formation, effect of social networks on recommendation systems, epidemiological models of information, cooperation and synchronization, effect of social networks on advertising systems. The workshop emphasizes the collection of free/open data from social networks, data processing and statistical analysis, with the aim of studying topologies and characteristics of various networks, identifying nodes of network influence, detecting communities with similar characteristics, studying information dissemination/opinion formation, systems and methods of social composition.