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Handbook of Graphs and Networks in People Analytics

Keith McNulty
€235.60
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A01=Keith McNulty
advanced people analytics with R and Python
Analytics
Assortativity Coefficient
Author_Keith McNulty
Basic Visualization
Category=GPH
Category=JHBC
Category=KJMV2
Category=PBT
Category=UFM
Category=UYZF
centrality measures
Closeness Centrality
Community Detection Algorithm
community detection techniques
Connected Components
Data Set
Data Sets
Dice Similarity Coefficient
Edge Sets
Eigenvector Centrality
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Eulerian Path
Examples
Graph Database
Graph Methodology
Graph Visualizations
Graphs
Handbook
Igraph Package
Induced Subgraph
Jaccard Similarity Coefficient
Keith
Largest Clique
Manual Calculation
Maximal Cliques
McNulty
network science methods
organisational structure analysis
Organizational Network Analysis
RDFs
relational data analysis
SFO
social network modelling
Undirected Graph
Vertex Properties
Weighted Graph

Product details

  • ISBN 9781032211244
  • Weight: 585g
  • Dimensions: 156 x 234mm
  • Publication Date: 20 Jun 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form.

The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level.

Key features:

  • Immediately implementable code, with extensive and varied illustrations of graph variants and layouts
  • Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation
  • Dedicated chapter on graph visualization methods
  • Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment
  • Various downloadable data sets for use both in class and individual learning projects
  • Final chapter dedicated to individual or group project examples
About Keith McNulty

Keith McNulty, PhD is a leading practitioner of applied mathematics, statistics, psychometrics and people analytics. He is currently Global Director of Talent Science and Analytics at McKinsey & Company.

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