SOCIAL MEDIA

Course Information
TitleΚΟΙΝΩΝΙΚΑ ΜΕΣΑ / SOCIAL MEDIA
CodeIW-07
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorIoannis Pitas
CommonNo
StatusActive
Course ID600000818

Programme of Study: PPS School of Informatics (2014-today)

Registered students: 5
OrientationAttendance TypeSemesterYearECTS
TECΗNOLOGIES GNŌSĪS DEDOMENŌN KAI LOGISMIKOUElective Courses217.5
TECΗNOLOGIES PLĪROFORIAS KAI EPIKOINŌNIŌN STĪN EKPAIDEUSĪElective Courses217.5
PSĪFIAKA MESA- YPOLOGISTIKĪ NOĪMOSYNĪElective Courses belonging to the selected specialization217.5
DIKTYAKA SYSTĪMATAElective Courses217.5

Class Information
Academic Year2015 – 2016
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
600011406
Type of the Course
  • Scientific Area
Course Category
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge in applied mathematics and graph theory. Programming skills. Good level of English.
Learning Outcomes
a) Knowledge: Familiarization with the fundamental principles, theory algorithms and technology of graph and social media theory. Exposure to programming using C/C++, MATLAB, or Scala for social media analysis. Acquaintance with subfields of graph analysis that are relative to subjects that concern social media. b) Skills: Setting the foundations for advanced studies on graph theory and social media, as well as on applications in social media-extracted information retrieval and processing. Promoting analytical and programming skills. Ability to develop basic social media-extracted data analysis applications using C/C++, MATLAB. Ability to develop distributed applications for processing big social media data using Scala programming language and through the Apache Spark distributed processing framework. Ability to gather and analyze data from social media.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Generate new research ideas
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Graphs in Social and Digital Media, Mathematical Preliminaries: Graphs and Matrices, Algebraic Graph Analysis, Web Search Based on Ranking, Label Propagation and Information Diffusion in Graphs, Graph-Based Pattern Classification and Dimensionality Reduction, Matrix and Tensor Factorization with Recommender System Applications, Multimedia Social Search Based on Hypergraph Learning, Graph Signal Processing in Social Media, Big Data Analytics for Social Networks, Semantic Model Adaptation for Evolving Big Social Data, Big Graph Storage, Processing and Visualization.
Keywords
graph theory, social media, social networks
Educational Material Types
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures1264.2
Reading Assigment421.4
Project571.9
Exams
Total2257.5
Student Assessment
Description
Literature surveys or programming assignments, presentations on which students are examined.
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Labortatory Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
“Graph-Based Social Media Analysis”, edited by Ioannis Pitas, CRC Press, 2016.
Additional bibliography for study
•"Exponential Random Graph Models for Social Networks: Theory, Methods and Applications (Structural Analysis in the Social Sciences)”, by Dean Lusher, Johan Koskinen and Garry Robins, Cambridge University Press, 2012. •“Social Network Analysis (Quantitative Applications in the Social Sciences)” by David Knoke and Song Yang, SAGE, 2007. •“Social Network Analysis and Education: Theory, Methods & Applications”, by Brian V. Carolan, SAGE, 2013. •“Analyzing Social Networks”, by Stephen P. Borgatti, Martin G. Everett and Jeffrey C. Johnson, SAGE, 2013. •“Large-Scale Data Analytics”, by Aris Gkoulalas-Divanis and Abderrahim Labbi, Springer, 2014. •“Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn and Other Social Media Sites”, by Matthew A. Russell, O’Reilly, 2011. •“Social Network Analysis for Startups: Finding connections on the social web”, by Maksim Tsvetovat and Alexander Kouznetsov, O’Reilly, 2011. •“Social Network Analysis: A Handbook”, by John Scott, SAGE, 2000. •“Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences)”, by Stanley Wasserman and Katherine Faust, 1994. •“Graph Mining: Laws, Tools, and Case Studies”, by D. Chakrabarti and C Faloutsos, Morgan & Claypool, 2012. The book is a rather short one and covers only few of the tools (e.g., tensors) and applications at hand (e.g., community detection, diffusion). •“Networks: an introduction” by M. Newman, Oxford University Press, 2010. •“Networks, Crowds, and Markets”, by Easley and Kleinberg, Cambridge University Press, 2010. •“Introduction to Computational Social Science”, by Claudio Cioffi-Revilla, Springer, 2014.
Last Update
01-04-2016