Παρουσίαση/Προβολή

Course Objectives/Goals

The aim is the study, analysis and mining from the world wide web and social networks. The course will focus on two axes, that of network analysis and web mining. The first axis focuses on the measurement, analysis and visualization of relationships and flows between entities participating in a network with emphasis on properties and applications on the web and social networks. In the context of web mining, there will be a study of methods and tools for content, structure and usage mining with emphasis on the management of non-relational data, such as semi-structured data in the form of graphs or even unstructured as text.

Course Content

• Introduction to network analysis
• The architecture of the Web
• Random networks, formation and evolution of social networks
• Centrality and other network metrics
• Homophily and community detection
• Link analysis and web search
• Influence, epidemics and information diffusion
• Graph embeddings and link prediction
• Knowledge graphs
• Social networks visualization
• Web mining
• Text mining-opinion mining
• Web usage mining
• Recommendation systems

Bibliography

• David Easley, Jon Kleinberg, “Networks, Crowds, and Markets -Reasoning about a Highly Connected World”, Cambridge University Press, 2010.
• Albert-László Barabási, "Network Science", 1st Edition, Cambridge University Press, 2016.
• Jure Leskovec, Anand Rajaraman, Jeff Ullman, “Mining of Massive Datasets”, 3rd Edition, Cambridge University Press, 2020.
• Bing Liu, “Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data”, Springer 2011.
• Mohammed Zuhair Al-Taie, Seifedine Kadry, “Python for Graph and Network Analysis”, Springer, 2017.
• Reza Zafarani, Mohammad Ali Abbasi, Huan Liu,  “Social Media Mining: An Introduction”, Cambridge University Press, 2014.
• Steve Borgatti, Martin Everett and Jeff Johnson, “Analyzing Social Networks”, 2nd Edition, Sage, 2018.
• Dmitry Zinoviev, “Complex Network Analysis in Python: Recognize - Construct - Visualize - Analyze – Interpret”, Pragmatic Bookshelf , 2018.

Assessment Methods

•  Projects/homework assignments 50%
•  Final written exam 50%