M222: Analysis of Social Networks


Announcements

  • Classes begin February, 22nd.

Class Coordinates

  • Meeting time: Tue at 18:00-21:00
  • Location: Classroom B (I'll setup a zoom meeting for the 1st lecture for those that can't make it. Use piazza to ask for details.)
  • Discussion Forum: Piazza

Prerequisites

  • Knowledge of basic computer science principles, sufficient to write a reasonably non-trivial computer program.

Course Description

Networks are a fundamental tool for modeling complex social, technological, and biological systems. The explosive growth of social networks, the continually-expanding WWW-space, and the large-scale data availability in biological sciences have led to algorithms and systems that routinely handle voluminous data modeled as graphs. This course will focus on the analysis of massive networks which provide several computational, algorithmic, and modeling challenges.

Topics to be covered include:

  • Networks Models
  • Network Centrality
  • Link Analysis
  • Network Construction and Inference
  • Pregel
  • Graph Compute Engines
  • Knowledge Graphs
  • Graph Databases
  • Community Detection
  • Link Prediction
  • Cascading Behavior

Course Objectives

In this course students will learn and exploit graph mining algorithms and tools to reveal insights on the social, technological, and natural worlds, by means of studying their underlying network structure and interconnections. A key element of the course will be the exposure to hands-on practice, through in-class and at-home assignments, with graph algorithms in the context of both Graph Compute Engines (Apache Spark GraphFrames) and Graph Databases (Neo4J). As the course progresses the students will familiarize themselves with increasingly more complicated graph mining tasks and will develop their own code to analyze real-world network datasets and come up with valuable insights.

Texts

  • Networks, Crowds, and Markets: Reasoning about a Highly Connected World
    David Easley and Jon Kleinberg
  • Network Science
    Albert-László Barabási, Márton Pósfai
  • Mining of Massive Datasets
    Jure Leskovec, Anand Rajaraman, Jeffrey Ullman
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