1. Identification of social networks and related analysis methods
2. Construction and analysis tools for data that are not in network form (text, image, etc.)
3. Learning of important properties and process characteristics on networks like community detection, sentiment analysis, information diffusion, network robustness, etc.
Course Content (Syllabus)
Centrality measures, network models (random) and characteristics (small world phenomena, power law distribution on various networks characteristics), mechanisms for network generation (preferential attachment), network robustness. Online social data sources, community detection, sentiment analytics basics, information diffusion, influence detection, fraud detection.
centrality measures, network models, community detection, dynamic processes on networks