M203. Social Network Analysis

1. Course Information:
Course Title: Social Network Analysis
Semester: 2nd
Hours per Week: 3
ECTS Credits: 6

2. Learning Objectives:
The course aims to familiarize and specialize students in the subject of network analysis, both from the perspective of analyzing graphs that describe social networks and from the perspective of analyzing texts published on social networks with the goal of extracting the author’s opinion. Specifically, students will acquire knowledge about graph theory, basic graph analysis algorithms, and text processing. They will specialize in using and applying graph databases for social network analysis, apply graph analysis methods for link and friend prediction, and understand, analyze, and implement text processing methods for opinion retrieval.

3. Course Subject:
The covered topics include:

  • Introduction:
    • Introductory concepts of Social Network Analysis
  • Graph Theory:
    • Basic concepts of graph theory
  • Collaborator Recommendation:
    • The problem of collaborator recommendation in a professional network. Concepts and algorithms.
  • Graph Databases:
    • Introduction to Graph Databases (Graph Databases)
    • Exercise and examples in the Graph Database “Neo4j”
  • Search in Social Networks and Opinion Retrieval: Requirements & Problems
  • Machine Learning and Natural Language Processing:
    • Basic concepts of natural language processing
    • The Word2Vec method
    • Applications
  • Sentiment Analysis:
    • Sentiment analysis in natural language texts, social networks, and microblogs (e.g., Twitter)
    • Methods and techniques for sentiment analysis
  • Friend Network Analysis in Social Networks:
    • Link prediction: friend recommendation and follow recommendation

4. Teaching Method:
Student education combines lectures and laboratory exercises. Students will be required to implement a project in one of the topics of the course using either machine learning methods for sentiment analysis in natural language texts or using a graph database for network analysis.

5. Student Evaluation Method:
Student evaluation is based on the final project they will submit at the end of the semester. Participation in project presentations also contributes to the final grade.

6. Equipment – Software Requirements:
The equipment required for student training in a laboratory environment is provided by the computer science department, and the software and tools used will be provided as open-source licenses.

7. Suggested Bibliography:

  • Graph Algorithms: Practical Examples in Apache Spark and Neo4j, Mark Needham and Amy E. Hodler, O’Reilly Media, 2019.
  • Αλγοριθμική θεωρία γραφημάτων, Νικολόπουλος, Σ., Γεωργιάδης, Λ., Παληός, Λ., 2015.
  • A First Course in Network Science, F. Menczer, S. Fortunato, and C. Davis, Cambridge University Press, 2020.
  • Social Network Analysis: Methods and Examples, S. Yang, F. B. Keller, L. Zheng, Sage Publishing, 2017.
  • Social Network Analysis, J. Scott, Sage Publishing, 2017.
  • Neural Network Methods in Natural Language Processing, Y. Goldberg, 2017.
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