Courses

Social and Economic Network Analysis

General

Course Contents

  1. Introductory concepts, graph, network, vertices, edges.
  2. Computer network representation, density, connectivity.
  3. Presentation of free software (gephi, nodexl).
  4. Shortest paths, algorithmic solving.
  5. Shorter paths, application in project management (PERT – CPM).
  6. Social Networks, modeling techniques.
  7. Important nodes (centrality metrics – calculations and algorithms).
  8. Important nodes – applications.
  9. Grouping (cliques, clans, communities), finding.
  10. Applications in the field of economics and finance.
  11. Applications in the field of administration and marketing.
  12. Presentation of projects.
  13. Course outline.

Educational Goals

Network Theory is a relatively new area of ​​Science within Operations Research and Graph Theory. Recent proposals in the literature consider that it is now a new paradigm. This course provides an introduction to the classic concepts of graph theory and network optimization. Next, newer concepts related to Social Network Analysis are introduced in terms of their structure. Particular emphasis is placed on the modeling of problems related to the general subject of Economics and Management, such as in the analysis of networks of employees – customers – companies, credit institutions, stocks, but also in the production of new information through data mining techniques.

After successful completion of this course, students will be able to:

  • Define the concepts graph, network and their components.
  • Know and apply classical optimization algorithms in networks.
  • Model classic business research problems in network form and will solve them (shortest – longest paths, PERT, CPM).
  • Realize the utility of representing interconnected units in various fields of the contemporary economic and social situation.
  • Know concepts related to the importance of specific nodes.
  • Analyze networks in terms of node importance using specific software.
  • Recognize the need for grouping and the various structures (cliques, cores, clans, communities) and gain knowledge and experience in how to search for them
  • Implement one or more social networks in the context of individual or group work and will study them macroscopically as well as at the node level.
  • Learn about link analysis.
  • Evaluate and classify specific networks into categories (small worlds, scale free, random).
  • Develop abilities to acquire new knowledge related to network theory and its manifestations in their science.

General Skills

  • Independent work.
  • Team work.
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology.
  • Decision making.
  • Working in an international environment.
  • Production of free, creative and inductive thinking.

Teaching Methods

  • In the classroom, face to face.

Use of ICT means

  • Basic software (windows, word, power point, the web, etc.).
  • Support of learning process through the electronic platform / e-class.

Teaching Organization

ActivitySemester workload
Lectures26
Practice Works13
Assignement (Essay writing)20
Independent Study66
Total125

Students Evaluation

Written final exams (60%) that may include:

  • Judgemental questions
  • Short answer questions
  • Application exercises
  • True/False and multiple choice questions
  • Composite theoratical questions

In each question, corresponding evaluation points are specified.
Optional assignment (Essay writing and presentation) corresponds to 40% of the final grade.

Recommended Bibliography

  1. Charles Kadushin, ΚΟΙΝΩΝΙΚΑ ΔΙΚΤΥΑ, 2019, ΚΡΙΤΙΚΗ
  2. Katharina A. Zweig, Network Analysis Literacy, 2016, Springer Vienna,
    https://service.eudoxus.gr/search/#s/social%20network%20analysis/0
  3. Νικολόπουλος Σταύρος, Γεωργιάδης Λουκάς, Παληός Λεωνίδας, Αλγοριθμική θεωρία γραφημάτων,
    https://repository.kallipos.gr/handle/11419/2067