Social network analysis for startups:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Cambridge ; Farnham ; Köln ; Sebastopol ; Tokyo
O'Reilly
[2011]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Auf dem Umschlag: finding connections on the social web Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xi, 174 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9781449306465 1449306462 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804150657081409536 |
|---|---|
| adam_text | Table
of
Contents
Preface
.............................................. —................. «.
vii
1.
Introduction
............................
,
...
,
.............
,
....___.... 1
Analyzing Relationships to Understand People and Groups
2
Binary and Valued Relationships
2
Symmetric and Asymmetric Relationships
3
Multimode Relationships
3
From Relationships to Networks
—
More Than Meets the Eye
3
Social Networks vs. Link Analysis
4
The Power of Informal Networks
6
Terrorists and Revolutionaries: The Power of Social Networks
10
Social Networks in Prison
10
Informal Networks in Terrorist Cells
11
The Revolution Will Be Tweeted
14
2.
Graph Theory
—
A Quick Introduction
..................................... 19
What Is a Graph?
19
Adjacency Matrices
21
Edge-Lists and Adjacency Lists
22
7
Bridges of
Königsberg 23
Graph
Traversals
and Distances
25
Depth-First Traversal
27
Breadth-First Traversal
30
Paths and Walks
31
Dijkstra s
Algorithm
33
Graph Distance
35
Graph Diameter
36
Why This Matters
36
6
Decrees of Separation is a Myth!
37
Smal·
World Networks
37
III
3.
Centralíty,
Power, and Bottlenecks
....................................... 39
Sample Data: The Russians are Coming!
39
Get Oriented in Python and NetworkX
39
Read Nodes and Edges from
Livejournal
41
Snowball Sampling
43
Saving and Loading a Sample
Dataset
from a File
44
Cen tra lity
45
Who Is More Important in this Network?
45
Find the Celebrities
45
Find the Gossipmongers
49
Find the Communication Bottlenecks and/or Community Bridges
51
Putting It Together
54
Who Is a Gray
Cardmal? 55
Klout Score
57
PageRank
—
-How Google Measures Centra
lity
58
What Can t Centrality Metrics Tell Us?
60
4.
Cliques, Clusters and Components
........................................ 61
Components and Subgraphs
61
Analyzing Components with Python
62.
Islands in the Net
62
Subgraphs
—
Ego Networks
65
Extracting and Visualizing Ego Networks with Python
65
Triads
67
Fraternity Study
—
Tie Stability and Triads
68
Triads and Terrorists
68
The Forbidden Triad and Structural Holes
72
Structural Holes and Boundary Spanning
73
Triads in Politics
74
Directed Triads
76
Analyzing Triads in Real Networks
77
Real Data
79
Cliques
79
Detecting Cliques
79
Hierarchical Clustering
81
The Algorithm
82
Clustering Cities
83
Preparing Data and Clustering
84
Block Models
86
Triads, Network Density, and Conflict
88
5.
2-Mode Networks
, — ................................................. 93
Does Campaign Finance Influence Elections?
93
iv
I Table of Contents
Theory of 2-Mode Networks
96
Affiliation Networks
96
Attribute Networks
98
A Little Math
98
2-Mode Networks in Practice
100
PAC
Networks
102
Candidate Networks
102
Expanding Multimode Networks
105
Exercise
107
6.
Going Viral! Information Diffusion
....................................... 109
Anatomy of a Viral Video
109
What Did Facebook Do Right?
110
How Do You Estimate Critical Mass?
111
Wikinomics of Critical Mass
112
Content is (Still) King
113
How Does Information Shape Networks (and Vice Versa)?
116
Birds of a Feather?
117
Homophily vs. Curiosity
117
Weak Ties
119
Dunbar Number and Weak Ties
119
A Simple Dynamic Model in Python
121
Influencers in the Midst
125
Exercises for the Reader
127
Coevolution of Networks and Information
127
Exercises for the Reader
133
Why Model Networks?
134
7.
Graph Data in the Real World
........................................... 137
Medium Data: The Tradition
138
Big Data: The Future, Starting Today
138
Small Data
—
Flat File Representations
139
EdgeList Files
139
.net Format
140
GML, GraphML, and other XML Formats
141
Ancient Binary Format
—
##h Files
142
Medium Data : Database Representation
142
W
h a t
a re Cursors?
14 3
What are Transactions?
144
Names
144
Nodes as Data, Attributes as
? 145
The Class
] 45
Functions and Decorators
146
fable of Contents
The Adaptor
148
Working with 2-Mode Data
150
Exercises for the Reader
151
Social Networks and Big Data
151
NoSQL
152
Structural Realities
153
Computational Complexities
156
Big Data is Big
156
Big Data at Work
156
What Are We Distributing?
157
Hadoop,
S3,
and MapReduce
157
Hive
158
SQL is Still Our Friend
160
A. Data Collection
___.................................................. « 161
B. Installing Software
..........___...___............................... 171
vi
I Table of Contents
Social
Network
Analysis for Startups
Does your startup rely on social network analysis? This concise guide
provides a statistical framework to help you identify social processes
hidden among the tons of data now available.
Social network analysis (SNA) is a discipline that predates Facebook
and Twitter by
30
years. Through expert SNA researchers, you ll learn
concepts and techniques for recognizing patterns in social media,
political groups, companies, cultural trends, and interpersonal networks.
You ll also learn
hov/
to use Python and other open source tools
—
such
as NetworkX, NumPy, and Matplotlib
—
to gather, analyze, and visualize
social data. This book is the perfect marriage between social network
theory and practice, and a valuable source of insight and ideas.
m
Discover how internal social networks affect a company s ability
to perform
г
*
Follow terrorists and revolutionaries through the
1998
Khobar
Towers bombing, the
9/11
attacks, and the Egyptian uprising
и
Learn how a single special-interest group can control the outcome
of a national election
■
Examine relationships between companies through investment
networks and shared boards of directors
■
Delve into the anatomy of cultural fads and trends
—
offline
phenomena often mediated by Twitter and Facebook
Purchase the ebook edition of this O Reilly title at oreilly.com and get
free updates for the life of the edition. Our ebooks are optimized for
several electronic formats, including PDF, EPUB,
Mobi,
APK,
and
DAISY—all DRM-free.
¡
Strata
is the emerging ecosystem of people,
.,>,
;|
ι
;;l
ţ)
tools, and technologies that turn big data
?v vf
۔
<
j n
:
¡nto smart decisions. Find information and
Making Data Work
!
resources at oreilly.com/data.
|
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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik Soziologie |
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| spelling | Tsvetovat, Maksim Verfasser (DE-588)1041308884 aut Social network analysis for startups Maksim Tsvetovat and Alexander Kouznetsov 2012 Beijing ; Cambridge ; Farnham ; Köln ; Sebastopol ; Tokyo O'Reilly [2011] xi, 174 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Auf dem Umschlag: finding connections on the social web Hier auch später erschienene, unveränderte Nachdrucke Soziale Software (DE-588)7550143-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Social Media (DE-588)4639271-3 gnd rswk-swf Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd rswk-swf Data mining Online social networks Data Mining (DE-588)4428654-5 s Netzwerkanalyse Soziologie (DE-588)4205975-6 s Soziale Software (DE-588)7550143-0 s Social Media (DE-588)4639271-3 s DE-604 Kouznetsov, Alexander Verfasser (DE-588)1041311648 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026194830&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026194830&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Tsvetovat, Maksim Kouznetsov, Alexander Social network analysis for startups Soziale Software (DE-588)7550143-0 gnd Data Mining (DE-588)4428654-5 gnd Social Media (DE-588)4639271-3 gnd Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd |
| subject_GND | (DE-588)7550143-0 (DE-588)4428654-5 (DE-588)4639271-3 (DE-588)4205975-6 |
| title | Social network analysis for startups |
| title_alt | 2012 |
| title_auth | Social network analysis for startups |
| title_exact_search | Social network analysis for startups |
| title_full | Social network analysis for startups Maksim Tsvetovat and Alexander Kouznetsov |
| title_fullStr | Social network analysis for startups Maksim Tsvetovat and Alexander Kouznetsov |
| title_full_unstemmed | Social network analysis for startups Maksim Tsvetovat and Alexander Kouznetsov |
| title_short | Social network analysis for startups |
| title_sort | social network analysis for startups |
| topic | Soziale Software (DE-588)7550143-0 gnd Data Mining (DE-588)4428654-5 gnd Social Media (DE-588)4639271-3 gnd Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd |
| topic_facet | Soziale Software Data Mining Social Media Netzwerkanalyse Soziologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026194830&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026194830&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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