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E-raamat: Handbook of Graphs and Networks in People Analytics: With Examples in R and Python

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  • Formaat: 268 pages
  • Ilmumisaeg: 19-Jun-2022
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781000597233
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Handbook of Graphs and Networks in People Analytics: With Examples in R and PythonSuurem pilt
  • Formaat: 268 pages
  • Ilmumisaeg: 19-Jun-2022
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781000597233
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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

Arvustused

It is exciting and inspiring to see the way McNulty explains network methods, as he unpacks the distinct elements and analytic steps to make them transparent. This makes it easier for readers to see how these elements fit together and apply to organizational challenges, sparking new ideas for innovative solutions. By demystifying this topic, McNulty empowers people to find their own solutions and engage in more productive conversations, regardless of who is writing the actual code or running the analyses. This book can help to democratize network analysis and improve the level of data fluency in organizations more generally.

- From the foreword by Professor Jeff Polzer, Harvard Business School

Foreword xi
Professor Jeff Polzer
Introduction xv
1 Graphs Everywhere!
1(14)
1.1 The Seven Bridges of Konigsberg
1(3)
1.2 Graphs as mathematical models
4(2)
1.3 Graph theory in the analysis of people and groups
6(5)
1.3.1 The study of connection
6(1)
1.3.2 The study of information flow
7(1)
1.3.3 The study of community, diversity and familiarity
8(1)
1.3.4 The study of importance, influence and attachment
9(1)
1.3.5 Graphs as data sources
10(1)
1.4 Purpose, structure and organization of this book
11(4)
2 Working with Graphs
15(28)
2.1 Elementary graph theory
15(13)
2.1.1 General definition of a graph
16(2)
2.1.2 Types of graphs
18(5)
2.1.3 Vertex and edge properties
23(2)
2.1.4 Representations of graphs
25(3)
2.2 Creating graphs in R
28(8)
2.2.1 Creating a graph from an edgelist
28(2)
2.2.2 Creating a graph from an adjacency matrix
30(1)
2.2.3 Creating a graph from a dataframe
31(2)
2.2.4 Adding properties to the vertices and edges
33(3)
2.3 Creating graphs in Python
36(5)
2.3.1 Creating a graph from an edgelist
36(2)
2.3.2 Creating a graph from an adjacency matrix
38(1)
2.3.3 Adding vertex and edge properties to a graph
39(2)
2.4 Learning exercises
41(2)
2.4.1 Discussion questions
41(1)
2.4.2 Data exercises
42(1)
3 Visualizing Graphs
43(40)
3.1 Visualizing graphs in R
44(30)
3.1.1 Native plotting in igraph
45(5)
3.1.2 Graph layouts
50(7)
3.1.3 Static plotting with ggraph
57(9)
3.1.4 Interactive graph visualization using visNetwork
66(3)
3.1.5 Interactive graph visualization using networkD3
69(5)
3.2 Visualizing graphs in Python
74(6)
3.2.1 Static plotting using networkx and matplotlib
74(3)
3.2.2 Interactive visualization using networkx and pyvis
77(3)
3.3 Learning exercises
80(3)
3.3.1 Discussion questions
80(1)
3.3.2 Data exercises
81(2)
4 Restructuring Data for Use in Graphs
83(36)
4.1 Transforming data in rectangular tables for use in graphs
84(18)
4.1.1 Creating a simple graph of a management hierarchy
85(2)
4.1.2 Connecting customers through sales reps
87(5)
4.1.3 Connecting customers through common purchases
92(5)
4.1.4 Approaches using Python
97(5)
4.2 Transforming data from documents for use in graphs
102(14)
4.2.1 Scraping data from semi-structured documents
103(4)
4.2.2 Creating an edgelist from the scraped data
107(3)
4.2.3 Approaches in Python
110(6)
4.3 Learning exercises
116(3)
4.3.1 Discussion questions
116(1)
4.3.2 Data exercises
117(2)
5 Paths and Distance
119(28)
5.1 Theory of graph traversal, paths and distance
120(9)
5.1.1 Paths and graph traversal
120(3)
5.1.2 Path length and distance
123(2)
5.1.3 Shortest path algorithms
125(2)
5.1.4 Graph diameter and density
127(2)
5.2 Calculating paths, distance, diameter and density
129(7)
5.2.1 Calculating in R
129(5)
5.2.2 Calculating in Python
134(2)
5.3 Examples of uses
136(9)
5.3.1 Facilitating introductions in a workplace
137(2)
5.3.2 Finding distant colleagues in a workplace
139(6)
5.4 Learning exercises
145(2)
5.4.1 Discussion questions
145(1)
5.4.2 Data exercises
145(2)
6 Vertex Importance and Centrality
147(22)
6.1 Vertex centrality measures in graphs
148(4)
6.1.1 Degree centrality
149(1)
6.1.2 Closeness centrality
149(1)
6.1.3 Betweenness centrality
150(1)
6.1.4 Eigenvector centrality
151(1)
6.2 Calculating and illustrating vertex centrality
152(7)
6.2.1 Calculating in R
152(2)
6.2.2 Calculating in Python
154(3)
6.2.3 Illustrating centrality in graph visualizations
157(2)
6.3 Examples of uses
159(7)
6.3.1 Finding `superconnectors'
159(4)
6.3.2 Identifying influential employees
163(3)
6.4 Learning exercises
166(3)
6.4.1 Discussion questions
166(1)
6.4.2 Data exercises
166(3)
7 Components, Communities and Cliques
169(32)
7.1 Theory of components, partitions and clusters
170(5)
7.1.1 Connected components of graphs
170(2)
7.1.2 Vertex partitioning
172(1)
7.1.3 Vertex clustering and community detection
173(1)
7.1.4 Cliques
174(1)
7.2 Finding components, communities and cliques using R
175(7)
7.2.1 Finding connected components of disconnected graphs
175(3)
7.2.2 Partitioning and community detection in R
178(3)
7.2.3 Finding cliques in R
181(1)
7.3 Finding components, communities and cliques using Python
182(6)
7.3.1 Finding connected components using Python
182(1)
7.3.2 Partitioning and community detection using Python
183(4)
7.3.3 Finding cliques in Python
187(1)
7.4 Examples of uses
188(9)
7.4.1 Detecting communities and cliques among Facebook friends
188(5)
7.4.2 Detecting politically aligned communities on Twitter
193(4)
7.5 Learning exercises
197(4)
7.5.1 Discussion questions
197(1)
7.5.2 Data exercises
198(3)
8 Assortativity and Similarity
201(14)
8.1 Assortativity in networks
201(4)
8.1.1 Categorical or nominal assortativity
202(2)
8.1.2 Degree assortativity
204(1)
8.2 Vertex similarity
205(3)
8.3 Graph similarity
208(1)
8.4 Calculating assortativity and similarity in Python
209(3)
8.5 Learning exercises
212(3)
8.5.1 Discussion questions
212(1)
8.5.2 Data exercises
213(2)
9 Graphs as Databases
215(16)
9.1 Graph database technology
216(4)
9.1.1 Labelled-property graphs
216(2)
9.1.2 Resource description frameworks
218(2)
9.2 Example: how to work with a Neo4J graph database
220(8)
9.2.1 Using the browser interface
221(4)
9.2.2 Working with Neo4J using R
225(2)
9.2.3 Working with Neo4J using Python
227(1)
9.3 Moving to persistent graph data in organizations
228(1)
9.4 Learning exercises
229(2)
9.4.1 Discussion questions
229(1)
9.4.2 Data exercises
229(2)
10 Further Exercises for Practice
231(8)
10.1 Friendships among Scottish teenage girls
231(2)
10.2 Interactions between dolphins in Doubtful Sound, New Zealand
233(1)
10.3 Character interaction in Victor Hugo's novel Les Miserables
233(1)
10.4 Communication between criminals involved in a drug importation operation
234(1)
10.5 Academic collaboration between network scientists
235(1)
10.6 Other sources of data for practice
236(3)
References 239(2)
Glossary 241(6)
Index 247
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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