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In this course you'll learn how to work with and visualize network data. You'll use the
igraph package to create networks from edgelists and adjacency matrices. You'll also learn how to plot networks and their attributes. Then, you'll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you'll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you'll see how to create interactive network plots using threejs.
Introduction to networksFree
In this chapter, you will be introduced to fundamental concepts in social network analysis. You will learn how to use the
igraphR package to explore and analyze social network data as well as learning how to visualize networks.What are social networks?50 xpCreating an igraph object100 xpCounting vertices and edges100 xpNetwork attributes50 xpNode attributes and subsetting100 xpEdge attributes and subsetting100 xpVisualizing attributes100 xpQuiz on attributes50 xpNetwork visualization50 xpigraph network layouts100 xpVisualizing edges100 xpQuiz on igraph objects50 xp
Identifying important vertices in a network
In this chapter you will learn about directed networks. You will also learn how to identify key relationships between vertices in a network as well as how to use these relationships to identify important or influential vertices. Throughout this chapter you will use a network of measles transmission. The data come from the German city of Hagelloch in 1861. Each directed edge of the network indicates a child becoming infected with measles after coming into contact with an infected child.Directed networks50 xpDirected igraph objects100 xpIdentifying edges for each vertex100 xpRelationships between vertices50 xpNeighbors100 xpDistances between vertices100 xpFinding longest path between two vertices50 xpImportant and influential vertices50 xpIdentifying key vertices100 xpBetweenness100 xpVisualizing important nodes and edges100 xpImportant vertices quiz50 xp
Characterizing network structures
This module will show how to characterize global network structures and sub-structures. It will also introduce generating random network graphs.Introduction50 xpForrest Gump network100 xpNetwork density and average path length100 xpGraph density quiz50 xpUnderstanding network structures50 xpRandom graphs100 xpNetwork randomizations100 xpRandomization quiz50 xpNetwork substructures50 xpTriangles and transitivity100 xpTransitivity randomizations100 xpCliques100 xpVisualize largest cliques100 xp
Identifying special relationships
This chapter will further explore the partitioning of networks into sub-networks and determining which vertices are more highly related to one another than others. You will also develop visualization methods by creating three-dimensional visualizations.Close relationships: assortativity & reciprocity50 xpAssortativity100 xpUsing randomizations to assess assortativity100 xpReciprocity100 xpAssortativity quiz50 xpCommunity detection50 xpFast-greedy community detection100 xpEdge-betweenness community detection100 xpCommunity quiz50 xpInteractive network visualizations50 xpInteractive networks with threejs100 xpSizing vertices in threejs100 xp3D community network graph100 xp
In the following tracksNetwork Analysis
DatasetsFriendship network dataFriendship network edge dataFriendship network node dataForrest Gump network dataMeasles network data
Associate Professor at UT Austin
James P. Curley is an Associate Professor in the Department of Psychology at University of Texas Austin in the Behavioral Neuroscience division. He received a Ph.D. from Cambridge University in 2003 and completed post-doctoral research at Cambridge University and Columbia University before becoming an Assistant Professor of Psychology at Columbia University in 2012. Dr. Curley’s current research interests are in studying the social dynamics of animals. Dr. Curley teaches several undergraduate and graduate courses, including R programming for Behavioral Scientists and Statistics & Research Design.