Information
Course Code, Timings, Location
IMN003miE: Wednesday 10:30-12:00, Irinyi 106
Course Description
Motivated by the empirical study of networked systems such as the Internet, social networks, economic systems, and biological networks, numerous models and techniques have been developed to understand, describe, and predict the behavior of these systems. The course aims to review the fundamental concepts of the field, its key results, such as small-world properties, degree distribution, community structure, random graph models, and models of network growth and dynamic processes in networks. An important objective is to examine the concepts and results through the investigation of concrete social and economic (financial) networks, providing insight into the modeling of complex networks.
Requirements
Attendance is highly recommended. The end-of-year grade is made up of the following elements: During the semester, you will have the opportunity to complete various assignments (mainly using colab / python notebook), this is 40% of the grade (class activity is included). There will be an end-year inclass test, which will account for 60% of the grade. A minimum of 50% must be achieved in total to pass. To pass the course, a minimum of 50% must be achieved.
Grade Boundaries
- 80-100% excellent (5)
- 70-80% good (4)
- 60-70% average (3)
- 50-60% pass (2)
Useful Links
Jackson, Matthew O. Social and economic networks. Vol. 3. Princeton: Princeton University Press, 2008
Newman, Mark EJ. "The structure and function of complex networks." SIAM review 45.2 (2003): 167-256.
Datasets: Available on Aaron Clauset's website here, Mark Newman's website here, and a large database can be found here
Syllabus
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Lecture 1
Motivation, introductory examples, review of basic concepts Slides for Lecture 1
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Lecture 2
Some important concepts in network research. Degree distribution, measures of centrality. Slides for Lecture 2
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Lectures 3-4
Random graphs I: Erdős-Rényi graphs (G(n,m), G(n,p)), Small-world graphs (Watts-Strogatz and Kleinberg models), Scale-free networks. Slides for Lectures 3-4
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Lectures 4-5
Random graphs II. The uniform and preferential attachment models. Slides for Lectures 4-5
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Lecture 6
Additional graph models and their generation. Community structure.
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Lectures 6-7
Community structure, Newman-modularity, community detection algorithms I. Slides for Lectures 5-6
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Lectures 7-8
Community structure, Newman-modularity, community detection algorithms, the stochastic block model (SBM). Slides for Lectures 7-8
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Lecture 9
Random walks, information flow, and infection spreading in networks. Slides for Lecture 10
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Lectures 10-11
Overview, additional models, and algorithms.
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Lecture 12
End- term examination. Selected chapters.