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Business Network Analytics Sep 12 th , 2017 Daning Hu Department of Informatics University of Zurich Business Intelligence Research Group F Schweitzer et al. Science 2009
Business Network
Analytics
Sep 12th, 2017
Daning Hu
Department of Informatics
University of Zurich
Business Intelligence Research Group
F Schweitzer et al. Science 2009
Business Network Analytics and Business
Intelligence
Network Modeling
& Analysis
Design
BI Applications
Economic
Networks
Social
Networks
Financial Markets,
Banking Systems…
Online Communities,
Consumer Social
Networks, …
Influence
Risk Contagion
(e.g., Bank run)
Social Contagion
(e.g., Word-of-
Mouth)
Monitoring, Analyzing, and
Simulating Contagious
Risks, …
Identification and
Prediction of Influence-
Driven Network Diffusion
Stop Contagious Failures in Banking Systems
During 2008 financial tsunami, which bank(s) we should inject capital first to stop contagious failures in bank networks? 3
Utilize Peer Influence in Online Social Networks
Influencer Marketing, Product Recommendation
Who are the most influential people?
What are the patterns of information diffusion?4
Develop Strategies to Attack Terrorist Networks
A Global Salafi Jihad Terrorist NetworkHu et al. JHSEM 2009
How to effectively break down a terrorist network?5
6
Business Networks Analytics and Applications
• Instructor: Prof. Dr. Daning Hu， BIN. 2.A.12
• TA: Xiao Li, BIN.2.A.24
• Email: [email protected]
• Credits: 5 ECTS credits
• Class Schedule:
http://www.ifi.uzh.ch/en/bi/teaching/Fall2017/NA.html
• Language: English
• Audience: Undergraduate and Master students
• Office Hours: Email for appointment, Room 2.A.12.
7
Grading and Course Goals
• 1. One course project (90%)
• 2. Active participation and interaction during the lectures and
tutorials (10%)
• The project report should include the following four major
components:
– Network/Relational Data Collection (15%)
– Network Data Processing and Modeling (20%)
– Network Visualization (15%)
– Network Analysis (30%)
8
Example 1: Network Data Collection
• Social Networks: Online communities, Social networking
websites, Personal blogs and micro-bloggings, online video
sharing websites. (e.g., Programmable Web)
• E-Business: Amazon Web Service, Ebay Data API, Taobao.
• Others: Financial, Education data sources: Stanford SNAP
Portal
9
Example 2: Network Data Processing Modeling
• Extract relations/links from raw data in database tables
• Model such relations/links into network data.
– Node data
– Link data
10
Example 3: Network Visualization
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ACLS
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AICO
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AIQUY
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ALL
ALL14
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ASH
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ATH
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MNTR
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SHLM
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SI
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SIVB
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SON
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SPG
SPI
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SPSS
SPW
SQAA
SR
SRCP
SRE
SRP
SRR
SRT
SRZ
SSCC
SSD
SSP
ST
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STAR
STBA
STD
STE
STEI
STI
STJ
STK
STLD
STM
STR
STT
STTX
STU
STXN
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SUG
SUHJY
SUN
SUNW
SUP
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SUSQ
SVC
SVELY SVM
SVU
SWBT
SWC
SWFT
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SWK
SWKS
SWM
SWN
SWS
SWX
SWY
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SXT
SY
SYK
SYKE
SYMC
SYMM
SYY
SZE
T
TAK227
TALK
TAPB
TBCC
TBI
TBL
TCB
TD
TDK
TDS
TDW
TDY
TE
TECD
TECH
TECUB
TEF
TEK
TEL229
TEL233
TEN
TER
TES235
TF1236
TFS
TFX
TG
TGT
TGX
THC
THO
THQI
TI
TIF
TII
TIN
TJX
TKR
TLAB
TLB
TLS
TM
TMCS.X
TMK
TMO
TMS
TMX
TNB
TNC
TNL
TNM
T-O243
TOC
TOK241
TOK242
TOL
TOSBF
TOT
TOY
TP
TQNT
TR
TRB
TRBS
TRH
TRI
TRMB
TRMK
TRN
TROW
TRST
TRW.X
TRY
TRZ
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TSG
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TSN
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TW
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ULTE
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UNH
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UNP
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USFC
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V
VAL
VALU
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VC
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VIAB
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VOL
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WMO
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WNC
WON
WOR
WPI
WPO
WPPGY
WR
WRI
WRNC
WSBC
WSC
WSM
WSO
WSTC
WTNY
WTS
WTSLA
WTW
WWE
WWW
WWY
WXS
WY
WYE
X
XEL
XL
XLNX
XOM
XRAY
XRIT
XRX
XTO
Y
YAM260
YELL
YHOO
YRK
YUM
ZBRA
ZION
ZIXI
ZLC
ZMH
ZNT
ZQK
ZUR261
11
Example 4: Network Analytics
A Microblogging Network: Who possesses the most
advantageous position in brokering information and knowledge in
this network?
12
Example 5: A Global Terrorist Network
How to effectively break down terrorist networks?
13
Business Network Analytics or Applications
• Recommender Systems:
14
Business Network Analytics or Applications
• Social Media based Marketing, Word-of-Mouth Effect
15
Computing Tools Required In Tutorials
• Database Management Software
– MySQL or other common DBMS such as MS SQL Server, Oracle, etc.
• Network Visualization Tool: NetDraw, R (Statnet or iGraph)
• Network Analysis Tool: R (Statnet or iGraph), or UCINet
Outline
16
Syllabus
Examples
Introduction
Social Network Analysis
Social Network Data Modeling and Analysis
* Some of the contents are adapted from Prof. James Moddy’s slides at Duke University, and Prof.
Jure Leskovec and Lada Adamic from Standford University
Introduction: Why Study Networks?
17
One of the most profound changes in today’s world is -
Decentralization
Economical: BitCoin, Blockchain, P2P lending, etc.
Social: Social Media News, Online Communities, Terrorist Cells, etc.
Technological: Open Source Software, Virtual Teams, etc.
The power, information, resources in real world networks are
becoming increasingly decentralized ->
Nodes are distributed more equally, Less hierarchical;
Good representations of entities and relationships in decentralized
systems.
The Focus: The Influence Mechanisms in Networks
18
An average individual (node) can affect system outcome by
influencing its linked peers.
This course will focus on the “influence” mechanisms that
network actors affect each other in terms of opinions,
behaviors, or risks through various ties, thereby changing the
network outcomes
Economic influence: Risk contagion, etc.
Social influence: Word-of-Mouth, observation learning, herding, etc.
Network outcomes: Bank run, stock market crash, product diffusions.
A “Random” History of Network Science
19
Mathematical foundation – Graph Theory1736
1930 Social Network Analysis and Theories
Sociogram: Network visualization
Six degree of separation
Structural hole: Source of innovation
Network Science Economic networks (Agent modeling & simulation)
Dynamic network analysis
BI applications: product diffusion in social media, recommendation systems
1990 (Physicists) Complex Network Topologies
Small-world model (e.g., WWW)
Scale-free model (“Rich get richer”)2000
2017
?
20
Network Science
• Network science is an interdisciplinary academic field which
studies complex networks such as information networks,
biological networks, cognitive and semantic networks, and
social networks. It draws on theories and methods including:
– Graph theory from mathematics, e.g., Small-world
– Statistical mechanics from physics, e.g., Rich get richer,
– Data mining and information visualization from computer science,
– Inferential modeling from statistics, e.g., Collaborative filtering
– Social structure from sociology, e.g., weak tie, structural holes
• Network science can be defined as "the study of network
representations of physical, biological, and social phenomena leading
to predictive models of these phenomena.”
A “Random” History: Math, Psychology, Sociology…
21
The study of networks has emerged in diverse disciplines as
a means of analyzing complex relational data.
Network science has its root in Graph Theory.
Seven Bridges of Königsberg written by Leonhard Euler in 1736.
Focusing on the properties of pairwise relations in a network
structure.
Social Network Analysis
Jacob Moreno, a psychologist, developed the Sociogram and to
“precisely describe the interpersonal structure of a group”.
Stanley Milgram (Small World Experiment: Six Degrees of
Separation, 1960s). Facebook: 5.28 steps in 2008, 4.74 in 2011.
“For the last thirty years, empirical social research has been
dominated by the sample survey. But as usually practiced, …, the
survey is a sociological meat grinder, tearing the individual from his
social context and guaranteeing that nobody in the study interacts
with anyone else in it.”Allen Barton, 1968 (Quoted in Freeman 2004)
Moreover, the complexity of the relational world makes it
impossible to identify social connectivity using only our intuition.
Social Network Analysis (SNA) provides a set of tools to empirically
extend our theoretical intuition of the patterns that compose social
structure.
The Origin of Modern Network Science:
Social Network Analysis
Social network analysis (SNA) is a set of relational methods for
systematically understanding and identifying connections /ties
/relationships among actors.
Social network analysis (SNA)
is motivated by a structural intuition based on ties linking social actors
is grounded in systematic empirical data
draws heavily on graphic theory and imagery
relies on the use of mathematical and computational models.
24
Jacob’s experiment is the
first to use Social
Network Analysis
Jacob Moreno’s experiment on Friendship Network
Social Network analysis lets us answer questions about social
interdependence. These include:
“Networks as Variables” approaches
• Are kids with smoking peers more likely to smoke themselves?
• Do unpopular kids get in more trouble than popular kids?
• Do central actors control resources?
“Networks as Structures” approaches
• What generates hierarchy in social relations?
• What network patterns spread diseases most quickly?
• How do role sets evolve out of consistent relational activity?
We don’t want to draw this line too sharply: emergent role positions can
affect individual outcomes in a ‘variable’ way, and variable approaches
constrain relational activity.
What Does Social Network Analysis Study?
26
Now…
Nodes Links
Social network People Friendship, kinship,
collaboration
Inter-organizational
network
Companies Strategic alliance, buyer-seller
relation, joint venture
Citation network Documents/authors Citations
Internet Routers/computers Wire, cable
WWW Web pages hyperlink
Biochemical network Genes/proteins Regulatory effect
… … …
Complex Networks in the Real World
27
Now…
• Universal modeling and analysis methods for complex network
data
• Shared vocabulary between fields: Computer Science, Physics,
Sociology, Economics, Statistics, Biology
• “Big” Data availability: Internet, mobile, bio, health, security…
• Impact/usage: social networking, social media, marketing, etc.
Our Approach
28
What Why How
Social network
analysis (Metrics)
Describe the changes
in network evolution
Temporal changes in
network topological
measures
Dynamic network
recovery
(Relational) data
mining
Econometric
identification of
casual Social and
Economic influence
Distinguishing
homophily
Confounding
factors
PSM, DID, RD,
etc.
Explanations
Combine social
science methods,
data mining, machine
learning with
econometric analysis
Predict link formation
Simulate the
evolution of networks
What: Social Network Analysis
Social network analysis (SNA) is a set of metrics and
methods for systematically describing, modelling, and
analyzing relationships among actors.
Social network analysis (SNA)
is motivated by a structural intuition based on ties linking
social actors
is grounded in systematic empirical data
draws heavily on graphic imagery
relies on the use of mathematical and/or computational
models.
What is a Network?
Tie/Link: Relationship
or interaction
between two nodes.
Actor/Node: Any entity in
a network
(person, system,
group, organization)
Basic Concepts in (Social) Network Analysis
• Node, Actor, Vertex V
• Tie, Link, Edges E
• Network, System G (V, E)
• A link can be (1) Binary or Valued, (2)
Directed or Undirected.
a
b
c e
d
Undirected, binary Directed, binary
a
b
c e
d
a
b
c e
d
Undirected, Valued Directed, Valued
a
b
c e
d
1 34
21
Nodes or Social Actors
• Social Network data consists of two linked classes of data:
Nodes and Links.
• Node Example: Products in a purchase newtork
• Actor Examples: people in a group, departments within in a
corporation, public service agency in a city, nation-states in
the world system. “Node” does not imply that they have
intention or the ability to “act”.
• Network nodes are most often people, but can be any other
unit capable of being linked to another (schools, countries,
organizations, personalities, etc.)
Links or Ties
• Actors (nodes) are linked to one another by social ties (links)
– Kinship, role-based, cognitive, affective, interactions, affliations
• Example of direct links in SNA (Wasserman/Faust 2008:17):
– Evaluation of one person by another (friendship, liking, or respect)
– Transfers of material resources (business transactions, lending or
borrowing things)
– Behavioral interaction (talking together, sending messages)
– Physical connection (a road, river, or bridge connecting two points)
Weight/Strength of Ties
Positive and Negative Weights
Two Modes of Social Network Analysis
One-mode Complete network
37
38
One-mode Ego network
Ego Network Analysis
• Ego Network Analysis combine the perspective of network analysis with the data of mainstream social science
• No computer assisted analysis needed
40
Two-mode Complete Network (Bipartite Graph)
41
Two-mode (Bipartite) Network Transformation
From Zan Huang et al., 2009, Management Science
42
Network Data Modeling: Adjacency Matrix
43
Network Distance (Weighted) Adjacency Matrix
44
Major Network Data Formats (in UCINet)
Real World Networks are Sparse Graphs
More Types of Networks
