Python Programming Language and Social Networks
- Being able to write Python programs covering basic need of data scientist.
- Understanding basic concepts of social network analysis
- Understanding how networks can contribute to the explanation of specific social, political, economic and cultural phenomena
- Mastering basic skills of working with SNA software Gephi, Pajek, R packages
- Acquaintance with biblographic network analysis software VosViewer, CitNetExplorer
- Able to evaluate and revise learned scientific methods and methods of activity.
- Able to store and manage data.
- Able to retrieve data from open statistical databases, archives, and other public sources.
- Able to analyze and visualize data with Python.
- Able to independently master new research methods, change the scientific and production profile of their activity.
- Python for data analysis: overviewa. Installing python b. Console and Python interpreter c. Command prompt and modules/scripts d. Editor
- Scientific computing with Python: managing data, code, and resulta. Scientific computing: the three “R”s b. Organizing a computational project c. Virtual environment
- Writing code: statements and modulesa. Statements, expressions, objects, operator
- Data wrangling: numpy and pandas
- Data collection: web scraping and APIs
- Visualizing data: matplotlib and seaborn
- Data preprocessing and modeling: scikit-learn
- Python for Social Network Analysis
- Network terminology and metrics. Describing, visualising and analysing networks. Software: Pajek, Gephi, R.1) Network terminology and basic concepts Definition of a network. Nodes (vertices) and links (arcs, edges). Directed and undirected relations. Representation of social relations as graphs. How to record network data: adjacency matrix, edge list, node list. 2) Network structure: centrality Egonetworks, k-neighbors. Centrality measures: in/out degree centrality; betweenness; closeness; eigenvector, Katz & PageRank. Centrality vs. centralization. Degree distribution and centralization measures. 3) Social capital What is social capital? Definitions of P.Bourdieu, J.S.Coleman, R.D.Putnam, A.Portes. Bonding and bridging; social capital formation; social capital and economic development. World Bank's programs with emphasis on social capital. Operationalization and measures of social capital. 4) Network structure: components and communities. Weak, strong, and giant components. Diffusion of information in networks. Real life examples. Network modularity. Community detection algorithms: local and global definitions, vertex dissimilarity. Clustering: hierarchical, partitional, spectral. Communities in real-life networks.
- Network in bibliometrics: using SNA for bibliographic search and analysis. Software: VosViewer and CitNetExplorer5) Networks in bibliometrics. Two-mode (bimodal) networks. Affiliation matrix. Weighted networks. Bibliometric networks: co-citation, bib-coupling, co-authorship. Journal networks. Timeline in bibliometric analysis.
- Network theory and applications. Network models. Software: Pajek, Gephi, R6) Network models: Random graphs (Erdos-Renuy), Small world, Preferential attachment (Power law). Network characteristics: shortest path, clusterization coefficient, degree distribution. 7) Diffusion of information in networks. Opinion formation. Social movements. Recruiting through networks. 8) Project presentations 9) Test
- Test 1. Statements, expressions, objects, operator
- Test 2. Data: collecting and wrangling
- Laboratory work. Collecting data online
- Test 3
- Individual project (written essay) with oral presentation
- In-class Participation
- Interim assessment (3 module)0.4 * Laboratory work. Collecting data online + 0.3 * Test 1. Statements, expressions, objects, operator + 0.3 * Test 2. Data: collecting and wrangling
- Interim assessment (4 module)0.4 * In-class Participation + 0.35 * Individual project (written essay) with oral presentation + 0.25 * Test 3
- McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
- Scott, J. (DE-588)132315661, (DE-576)299070239. (2009). Social network analysis : a handbook / John Scott. Los Angeles [u.a.]: Sage. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.307646734
- Bernard, J. (2016). Python Recipes Handbook : A Problem-Solution Approach. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174476
- Downey, A. (2012). Think Python. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=477161
- Hajba G.L. Website Scraping with Python: Using BeautifulSoup and Scrapy / G.L. Hajba, Berkeley, CA: Apress, 2018.