MINF4533: Business Networks Analysis and Applications00000000-408a-7877-ffff-ffffd638d253/... · "...
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MINF4533: Business Networks Analysis and Applications Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 15th, 2014
Transcript of MINF4533: Business Networks Analysis and Applications00000000-408a-7877-ffff-ffffd638d253/... · "...
MINF4533: Business Networks Analysis and Applications
Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 15th, 2014
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Daning Hu: A Network Perspective
Daning Hu studied at
advises
works at
advises
collaborates
Dr. J. Leon Zhao
Jiaqi Yan
John
collaborates
collaborates
Social Network Analysis Financial Intelligence
Jiaqi Jackie Yan, 29, Male, Chinese
?
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Business Networks Analysis and Applications
n Instructor: Prof. Dr. Daning Hu， BIN. 2.A.12 n Co-Instructor: Dr. Jiaqi Yan， BIN. 2.B.24
n Email: [email protected] (In all emails to me, please put MINF 4533 in the subject line. This can me get to you faster).
n Credits: 5 ECTS credits
n Class Schedule: http://www.ifi.uzh.ch/bi/teaching/fall2014/lecture.html
n Language: English
n Audience: Undergraduate and Master students
n Office Hours: Email for appointment, Room 2.A.12.
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Course Plan
n This lecture mainly consists of two stages. ¨ 15.09 to 19.09: the instructors will introduce the basics of network
modeling and analysis techniques. The students will gain hands on experiences in network analysis in tutorials.
¨ 20.09 to 09.11: the students will work on and finish the midterm project (due 11:59 PM on Nov 09, 2014).
¨ 10.11 to 14.11: the instructors will introduce various business network applications in marketing and finance.
¨ 15.11 to 05.01.15: the students will work on and finish the final project (due 11:59PM on Jan 05, 2015).
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Grading and Course Goals n 1. Two course projects. One midterm and one final project (each
taking 40%, total is 80%) n 2. Active participation and interaction during the lectures and
tutorials (20%)
n The project report should include the following four major components: ¨ Network/Relational Data Collection (7.5%) ¨ Network Data Processing and Modeling (10%) ¨ Network Visualization (7.5%) ¨ Network Analysis (15%)
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Example 1: Network Data Collection
n Social Networks: Online communities, Social networking websites, Personal blogs and micro-bloggings, online video sharing websites. (e.g., Programmable Web)
n E-Business: Amazon Web Service, Ebay Data API, Taobao.
n Others: Financial, Education data sources: Stanford SNAP Portal
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Example 2: Network Data Processing Modeling
n Extract relations/links from raw data in database tables
n Model such relations/links into network data. ¨ Node data ¨ Link data
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Example 3: Network Visualization
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WLP
WLV
WM
WMB
WMI
WMO
WMS
WMT
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
9
Example 4: Network Analysis
n A Microblogging Network: Who possesses the most advantageous position in brokering information and knowledge in this network?
10
A Global Terrorist Network
n How to effectively break down terrorist networks?
11
Network-based Business Intelligence Applications n Recommender Systems:
12
Network-based Business Intelligence Applications n Reputation Systems:
13
Network-based Business Intelligence Applications n Social Media based Marketing, Word-of-Mouth Effect
14
Computing tools required for this course
n Database Management Software ¨ MySQL or other common DBMS such as MS SQL Server, Oracle, etc.
n Network Visualization Tool: NetDraw, R (Statnet or iGraph)
n Network Analysis Tool: R (Statnet or iGraph), or UCINet
