ECE434 Complex Networks

The contents of the course are the following:

• node, edge, graph theory, power laws, degree distribution
• random graph, network growth/evolution models, preferential attachment, network dynamics
• small world, six degrees of separation, scale-free networks
• degree centrality, closeness centrality, betweenness centrality, flow centrality, spectral centrality
• communities (overlapping, non-overlapping, graph-theoretic, spectral), modularity, clustering coefficient
• influence and susceptibility, influential spreaders
• robustness, temporal complex networks

The course comprises a detailed presentation of the modern field of complex networks investigating issues relevant to the iranalys is at the no delevel,at the group of nodes level, dissemination and spreading of information.

The goal of the course is to offer the knowledge and understanding of concepts, algorithms, and methodologies required for the analysis of large (technological, biological, social) networks.

The students taking this course will:

• Understand the concepts of Network Science.
  Familiarize themselves with methods for the analysis of large complex networks, especially those methods based on graph theory.
• Familiarize themselves with networks analysis software.
  Get an introduction to the field of Big Data.