Network Data Science
This course will introduce methods for analyzing and understanding the structure and function of networks, including social networks, web graphs, and sensor networks. The course will introduce students to the math and science of network analysis. Through real world examples, including analysis of their own networks, students will develop skills for describing and understanding the structure, patterns, and functionality of networks. Students will read classic and cutting edge articles and books about these topics and discuss their applicability to various network types. The class will culminate with a capstone project in which students will apply the analysis methods they have learned to understanding a particular question about a network they choose.
Objectives and Learning Outcomes
 Understand to the basic concepts of network analysis (Evaluation: problem sets)
 Collaborate with peers to apply these methods to a variety of networks (Evaluation: projects)
 Understand the link between qualitative and quantitative methods of network analysis (Evaluation: short analysis papers)
Course Outline
The course will be a combination of lecture, handson exercises, and discussion. Assignments will include the application of technologies to given tasks, analysis and writing of results on small projects, and a larger course capstone project that integrates the types of insights and analysis learned in class to study a specific type of network
 Network basics  structure, nodes and edges, centrality measures
 Network Visualization  how to represent and visualize networks
 Edge features  edge weight, tie strength, trust, interaction frequency, and other measures
 Network Propagation
 Small Worlds and Power Laws
 Temporal and Dynamic Networks
 Building networks  how to take observations and create network structure to analyze
Project
This course has a endofsemester group project focusing on the fake news ecosystem. The project outline is here:Class Material
 Properties of Networks
 Network Analysis and Data Collection
 Random Graphs

Random Graphs, Power Laws, and Mixing
 Lecture Slides
 Homework, Answers
 Extra Notes
 Network Science, The ScaleFree Property by Barabasi
 Fast unfolding of communities in large networks by Blondel et al.

Diffusion, Cascades, and Clusters
 Diffusion Slides
 Clusters
 Jupyter Notebooks
 Diffusion Simulation – example networks for propagation and diffusion. You can modify diffusion parameters and explore how they affect propagation speed
 Diffusion Game – Interactive game for the class in which groups decide which nodes to infect or immunize, and we explore how each set of decisions affects propagation
 Modularity Examples – Examples for communities and spectral clustering
 Dynamics Networks
Books
 Analyzing the Social Web, by Jennifer Golbeck
 Please also get the Errata here.
Evaluation
 50% Homework assignments (problem sets, papers, short analysis projects)
 25% Class Participation (includes inclass exercises)
 25% Final Project
Homeworks
Late policy: for assignments turned in a day or less late, you will lose 25% of your grade. Assignments more than a day late will not be accepted.
Class Participation
Class discussion will be an important part of this class running well. You will be assigned minimum participation requirements in the discussion boards, but you are encouraged to participate more. In class exercises are a required and important part of participation.
Academic Integrity
The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council. This Code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu.