Share Description
The learner will be introduced to network analysis through tutorials using the NetworkX library in this course. The course begins with an understanding of network analysis and the motivations for modelling phenomena as networks. The concept of connectivity and network robustness is introduced in the second week. The third week will look at methods for determining the importance or centrality of a node in a network. The final week will look at network evolution over time, as well as models of network generation and the link prediction problem.
This course should be taken after the following courses: Introduction to Data Science in Python, Applied Plotting, Charting, and Data Representation in Python, and Applied Machine Learning in Python.
Syllabus :
1. Why Study Networks and Basics on NetworkX
- Networks: Definition and Why We Study Them
- Network Definition and Vocabulary
- Node and Edge Attributes
- Bipartite Graphs
- TA Demonstration: Loading Graphs in NetworkX
2. Network Connectivity
- Clustering Coefficient
- Distance Measures
- Connected Components
- Network Robustness
- TA Demonstration: Simple Network Visualizations in NetworkX
3. Influence Measures and Network Centralization
- Degree and Closeness Centrality
- Betweenness Centrality
- Basic Page Rank
- Scaled Page Rank
- Hubs and Authorities
- Centrality Examples
4. Network Evolution
- Preferential Attachment Model
- Small World Networks
- Link Prediction
