Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Graph Terminology

A graph is $G = (V, E)$ consisting of vertices/nodes and edges/links.

Graph links can be represented in a matrix with individual nodes labelling the rows and columns.

An attributed graph has features associated with each node which can be captured in a data frame.

Graphs may be used for social networks where the nodes represent users, the edges identify friendship or following, the node attributes identify the user features and the node class labels are user groups or tags. Publicly available datasets include flickr and blogcatalog.

Graphs may be used for citation networks where the nodes represent research papers, edges capture citation links, the node attributes are the keywords vectors generated from the research paper or its abstract, and the node class labels capture the area of research. Publicly available datasets include cora, citeseer, and pubmed.