on mining big data and social network analysis · 2018-05-03 · background data are being...
TRANSCRIPT
On Mining Big Data
and Social Network
Analysis
Philip S. Yu ([email protected])Distinguished Professor & Wexler Chair
University of Illinois at Chicago
Background
Data are being generated and collected in
unprecedented ways and speed, e.g.,
• Sensors
• Web access log
• Electronic medical record
• Scientific instrumentation
• Transaction processing
• Blogging & social networking
• etc
3
Source: - What happens in an Internet Minute? (by Intel):
http://www.intel.com/content/dam/www/public/us/en/images/illustrations/embedded-infographic-600-logo.jpg
Why Concerned about Data?
Data is a tremendously valuable asset
o Corporation:
• Customer data can provide competitive edge
• Product review data is critical to the success of a product
o Government
• Public opinion is crucial to develop policies
• Integrated intelligence data is essential to fight crime
and terrorist attacks
o Healthcare
• Patient data is essential to move toward personalized
medicine
o Science
• New discoveries are driven by the availability of massive
amount of data
Big Data Characteristics• Volume/size
• Velocity: Rate/speedo Real-time processing requirement
• Data stream
• Varietyo Complex data
• High dimensionality
• Non-traditional data types
o Variability
o Heterogeneous sources and data types
o Cleanness: noisy, uncertain and incomplete
• Veracityo Trustworthiness
o Privacy preservation
• Valueo Low value density
o Weak signal
Hydraulic Fracturing to extract Shale Gas
Big Data Challenges
• Storage
• Indexing
• Retrieval
• Search
• Backup and restore
• Mining and analysis
• Privacy protection
Information Fusion• Fusing information across multiple sources is
the Holy Grail of big data research
• Many commercial companies have multiple sources of collecting customer information• Google has Google search, G-mail, Google Maps,
Google+, YouTube, etc.
• Other examples• Detection of terrorist plots
• Whereabouts on Malaysia MH370
• Focus on fusing multiple social networks
Social Network• Huge size
o Facebook: more than a billion nodes
• High volume of new content generatedo Rapidly and dynamically changing focus
• Rich information with many different types of data
• Noisy
• High aggregate value, but challenging to mine
Background• Many social networks with different objectives
• Foursquare
• YouTube
• Google+
• Individuals often participate in multiple social
networks
• Each social network only capture a partial or
biased view of an individual
Issues
• How to connect the multiple accounts of the
same users in different social networks?
• How to transfer knowledge across different
social networks?
Anchor Links across Aligned Networks
Locations
Tips
User Accounts
Temporal Activities
Ne
w
User Accounts
Locations
locate
locate
Tweets
Temporal Activities
Old
target networksource
network
Solve Challenge 1:
Heterogeneous relations
Social
Links
Contents:
Tweets
Location
s
Temporal
Activities
Social Network: Who Where What When
Social
Links
Contents:
Tweets
Location
s
Temporal
Activities
Solve Challenge 2:
Lack of Training Instances
training set Prediction
Solve Challenge 3:
Information Distribution
Difference Problem
Extract Heterogeneous Features
Social
Spatial
Temporal
Content
?
training set Prediction
Solve Challenge 4:
Inter-dependency on
relatons
Social
Spatial
Temporal
Content
?
location links
Social
Spatial
Temporal
Content
?
social links
?
?
Problem Description:
Anchor Link Prediction
IssuesTraining/learning:
Feature Selection
Testing/Inference:
One to one Constraint
More Compete Matching is Better
Summary• The Big Data revolution will fundamentally
change not only how we conduct business, but
also how we live our life
• Information fusion is the holly grail of big
data research
• Social network is a new way of interaction
among people
• Fusion information across social network is
most challenging