SubjectsSubjects(version: 845)
Course, academic year 2018/2019
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Social network and their analysis - NAIL116
Title in English: Sociální sítě a jejich analýza
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018 to 2018
Semester: winter
E-Credits: 6
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: English
Teaching methods: full-time
Guarantor: doc. RNDr. Iveta Mrázová, CSc.
RNDr. František Mráz, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Informatics, Software Applications, Computer Graphics and Geometry, Database Systems, Didactics of Informatics, Discrete Mathematics, External Subjects, General Subjects, Computer and Formal Linguistics, Optimalization, Programming, Software Engineering, Theoretical Computer Science
Annotation -
Last update: RNDr. Jan Hric (07.05.2018)
The concept of social networks is widely used to model mutual relationships between people (but also between other objects like chemical compounds). Intriguing problems from this area range from finding important structural patterns that influence interaction among the considered actors across sentiment analysis that studies people´s opinions, emotions, and attitudes to the analysis and evolution of the network structure itself. Recently, the trends have shifted rather towards online social networks (e.g., Facebook, LinkedIn and MySpace) which allow for efficient data collection.
Aim of the course -
Last update: RNDr. Jan Hric (07.05.2018)

The course reviews fundamental paradigms and algorithms used in the area of social networks. An important part of the lecture/seminar will represent the design and implementation of an own application in order to facilitate deeper understanding of both the social networks and the means applicable to their analysis. Knowledge at the extent of the bachelors´ course NDBI025 Database systems is expected as well as knowledge of the Python language at least on the level of the introductory course in programming for year 1 of the bachelor study program.

The course is given (only) in English.

Literature -
Last update: RNDr. Jan Hric (29.04.2018)
  • Charu C. Aggarwal (Ed.): Social Network Data Analytics, Springer, 2011
  • Amy N. Langville, Carl D. Meyer: Google´s PageRank and Beyond: The Science of Search Engine Rankings, Princeton University Press, 2006
  • Bing Liu: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Cambridge University Press, 2015

Syllabus -
Last update: RNDr. Jan Hric (07.05.2018)
  1. Introduction to the area:
    • Information retrieval, web search, social network analysis
    • Main areas of interest in social networks and future directions
  2. Fundamental paradigms of social network analysis:
    • Data and model description, text and web page pre-processing, graph data models, static and dynamic graph properties
    • Subgraph isomorphism, maximum common subgraph problem
    • Matching and distance computation in graphs
    • Topological descriptors for graph structures
    • Frequent substructure-based transformations and mining in graphs
    • Graph clustering and graph classification
  3. Techniques for web data mining:
    • Web crawling and resource discovery
    • Search engine indexing and query processing, web spamming
    • Ranking algorithms: PageRank and HITS
    • Recommender systems: content-based methods, collaborative filtering, graph-based methods, clustering methods, latent factor models
    • Web usage mining: data collection and pre-processing, discovery and analysis of web usage patterns
  4. Approaches to social network analysis:
    • Introduction and main properties, measures of centrality and prestige
    • Community detection: Kernighan-Lin algorithm and its analysis, clustering algorithms, overlapping communities
    • Node classification and label propagation, social influence analysis, detection of experts in social network
    • Evolution and link prediction in social networks
  5. Applications:
    • Sentiment analysis
    • Opinion mining - Feature-based opinion mining and summarization, opinion search and opinion spam
    • Data, text and multimedia information mining in social networks
    • Advertizing on the web
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