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Tuesday, 2017-03-28

Social Network Analysis

The study of networks, including computer networks, biological networks and in particular social networks has gained an enormous interest in the past few year. In this context, (social) network analysis is concerned with models, techniques, and algorithms to represent, analyze, and extract meaningful patterns from network data and social networks such as Facebook or Twitter. The objective of this course is to study the principles underlying (social) networks including how relationships between objects in a network are formed, how communities are built, and how networks and information within networks evolve over time. An important focus will be on approaches to explore and analyze diverse types of networks. In the exercises, students will learn to apply the techniques covered in class to different types of networks and applications using publicly available tools such as R, NetworkX, and igraph.

Main Topics:

  • Foundations of Networks
  • Graph Theory and Graph Algorithms
  • Networks Measures: Centrality, Transitivity, Reciprocity, Similarity
  • Network Models: Random Graphs, Small World Model, Preferential Attachement Model
  • Data Mining Essentials
  • Community Analysis: Detection, Evolution, Evaluation
  • Information Diffusion: Herd Behavior, Information Cascades, Epidemics
  • Applications: Influence and Homophily, Recommendation in Social Media, Behavior Analysis

Time and Place:

Lecture Monday, 2-4 pm, INF 350 (OMZ), Room U014

Thursday, 9-11am, INF 288, HS 2

Exercises Thursday 12-2pm, INF 350 (OMZ), Room U013 (Start: October 23)

Registration for the exercises is done through Musli (https://www.mathi.uni-heidelberg.de/muesli)

Suggested Prerequisites: Data Mining, Efficient Algorithms, Statistics

Literature: (1) R. Zafarani, M. A. Abbasi, H. Liu. Social Media Mining. Cambridge University Press, 2014. (2) M.E.J. Newman. Networks–An Introduction, Oxford University Press, 2010.

Grading: The requirement for receiving the 8 ETCS for this lecture is the successful participation in the exercises (more than 50% of the points in the assignments must be obtained) and the successful completion of a written final exam.

Audience: Students with Computer Science in major or minor course of study; students from CS related disciplines

Further Information: Prof. Dr. Michael Gertz, gertz@informatik.uni-heidelberg.de, INF 348, Room 12b. All materials for this lecture will be made available in Moodle at elearning2.uni-heidelberg.de/course/view.php

 

Letzte Änderung: 19.03.2015