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User Profiling in Ego-network : Co-profiling Attributes and Relationships. Rui Li, Chi Wang, Kevin Chen- Chuan Chang University of Illinois at Urbana-Champaign. User Profiling , which infers users’ attributes, is important for P ersonalized S ervices. User. Personalized Search.

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user profiling in ego network co profiling attributes and relationships

User Profiling in Ego-network:Co-profiling Attributes and Relationships

Rui Li, Chi Wang, Kevin Chen-Chuan Chang

University of Illinois at Urbana-Champaign

user profiling which infers users attributes is important for p ersonalized s ervices
User Profiling, which infers users’ attributes, is important for Personalized Services

User

Personalized Search

Search Engines

Richard

Targeted Advertisement

Advertisers

College: UIUC

Location: Champaign

and many others.

user profiling is crucial for s ocial a nalysis ability to survey the world
User Profiling is crucial for Social Analysis– Ability to survey the world

Surveying people for behavior:

  • How do college students like iPad vs. Galaxy?
  • How do California age 50+ males like ObamaCare?

Surveying behavior for people:

  • What demographics of users like Samsung more than Apple?
  • What communities of people support ObamaCare?
can we profile users missing attributes in social network
Can we profile users’ missing attributes in social network?

Employer: Yahoo! College: Berkeley

Employer: Twitter College:UIUC

Employer: Google College:UIUC

Employer: ? College:?

Employer: ? College:?

Employee: ?

College:?

Employer: Yahoo! College: Stanford

Employer: Twitter College: Berkeley

Employer: ?

College:?

Employee: JP Morgan

College: UIUC

Some users provide attributes in their online profiles

Some users’ attributes are missing

thus we abstract our problem as profiling users attributes based on friends attributes
Thus, we abstract our problem as profiling users' attributes based on friends’ attributes
  • Input:
  • a network G(V, E) ,
  • some users’ attributes
  • Output:
  • users’ attributes

Employer: Yahoo!

College:UIUC

Employer: Yahoo! College: Berkeley

Employer: Twitter College:UIUC

Employer: ? College:?

Employer: ?

College:?

Employer: Yahoo! College: Stanford

Employer: Twitter College: Berkeley

Employer: ?

College:?

Employer: JP Morgan

College: UIUC

while attributes may propagate across links links are very noisy
While attributes may “propagate” across links—Links are very noisy.

Existing methods simply assume that two connected users share the same value for any attribute

Employer: Yahoo! College: Berkeley

Employer: Twitter College:UIUC

Employer: Google College:UIUC

Employer: ? College:?

Employer: ? College:?

Employer: ?

College:?

Employer: Yahoo! College: Stanford

Employer: Twitter College: Berkeley

Employer: ?

College:?

Employer: JP Morgan

College: UIUC

However, users connect to friends with different values for an attribute

  • About 11% friends share the employer and 18% friends share the college.
  • Only 20% may have attributes.
why noisy every link is for a different relationship
Why noisy? Every link is for a (different) relationship!

Users have different types of relationships in real life.

Richard and Bob share the same employer, but may have different values for other attributes.

Colleagues

Bob

College classmates

Richard and Cindy share the same college, but may have different values for other attributes.

Cindy

Richard

Richard and Peter share the same interests, but may have different values for other attributes.

Club friends

Peter

on the other hand relationship profiling is necessary by itself and similarly challenged
On the other hand, Relationship Profiling is necessary by itself, and similarly challenged!
  • Link: Why does a link happen?
    • Given a link, what friendship does it represent?
  • Circle: Who form what circles?
    • Where are my circles?
    • What does each circle represent?
  • Challenge: While links/circles depend on attributes to detect and to explain, attributes are often unknown.
proposal co profiling attributes and relationships
Proposal: Co-profiling Attributes and Relationships
  • Attributes– properties of nodes
  • Relationships– properties of links
  • Together, understanding both nodes and links.

Why together?

1. Necessity: Dependency on each other to decide.

2. Benefit: Useful to know both!

Employer: Yahoo! College: Berkeley

Missing

Missing

Employer: Google College: UIUC

Employer: Yahoo!

colleagues

College: UIUC

classmates

slide10

But how?

Observing how

attributes and relationships

relate.

insight correlation between attributes and connections through relationship
Insight: Correlation between attributes and connections through relationship

Discriminative Correlation Insight : Attributes and connections are discriminatively correlated via a hidden factor -- relationship

To concretize our insight, we explore two dependencies based on a real-world user study.

  • Attribute-Relationship Dependency: How users’ attributes are related to hidden relationship types?
  • Connection-Relationship Dependency: How connections are related to hidden relationship types?
observation 1 attribute relationship dependency
Observation #1: Attribute-Relationship Dependency
  • Friends do not share all attributes.
  • What attributes they share depend on relationship.

The percentages of friends sharing the same value with the ego for different attributes overall of different relationship types.

observation 2 connection relationship dependency
Observation #2: Connection-Relationship Dependency
  • Friends do not connect to all friends.
  • What friends they connect to depend on relationship.

The average connections per user within and across three different relationships types

specifically w e focus on co profiling upon each user s ego network
Specifically, we focus on co-profiling upon each user’s ego-network
  • Ego-network: a subnet that around an individual user.

Association Vector

w1=<1, 0, 0, 0, 0, 0, 0>

w2=<0, 1, 0, 0, 0, 0, 0>

Employer: Yahoo! College: Berkeley

Employer: Twitter College:UIUC

Circle 2:friends likely to share college

Circle1:friends likely to share employee

f4=<0, 1, 0, 0, 0, 1, 0 0.1>

Attribute Vector

f1=<1, 0, 0, 1, 0, 0, 0.1>

Employer: Yahoo

College:UIUC

Employer: Google College:UIUC

Employer: ? College:?

x4=2

Circle Assignment

x1=1

Employer: ? College:?

Employer: Yahoo! College: Stanford

Employer: ?

College:?

f3=<1, 0, 0, 0, 1, 0, 0.1>

Employee: Twitter College: Berkeley

x3=1

Circle 3:friends likely to share other attribute

solution overview we realize co profiling in an optimization framework
Solution Overview: we realize co-profiling in an optimization framework

Partially Observed

User Attributes

Observed

User Connections

Unobserved

Friends’ circles

  • Cost Function: capturethe dependences between the variables based on the insight
  • Algorithm: finds the unknown variable that best satisfy the dependences
cost function we design a cost function to model the dependencies between variables
Cost Function: we design a cost function to model the dependencies between variables

Attribute-Relationship (circle) Dependency

Connection-Relationship Type (circle) Dependency

However, the function can not be optimized directly, as there are both discrete and continuous variables

There are other formulas to model the dependencies.

algorithm we minimize the function via updating each group of variables
Algorithm: we minimize the function via updating each group of variables
  • Update User Attribute Vectors F
  • Only propagate values from friends in the same circles
  • Only propagate the attribute value associated with the circle
  • Update User Circle Assignments X
  • Cosider both user’s attributes and connections
  • Update Circle Association Vectors W
  • Make association vector sparse
experiment w e first collect real world ego networks to evaluate our data set
Experiment: we first collect real-world ego-networks to evaluate our data set
  • We conduct user studies to collect users’ attributes and relationship types (circles) from LinkedIn.
  • Most users are have three attributes
  • 8K connection are labeled
  • We share the data online
    • https://wiki.engr.illinois.edu/display/forward/Dataset-EgoNetUIUC-LinkedinCrawl-Jan2014
experiment we evaluate our algorithm on both attribute and relationship type profiling
Experiment: we evaluate our algorithm on both attribute and relationship type profiling
  • Attribute Profiling
    • APw: a classic collective classification approach, which profiles a node’s label using weighted votes from its neighbors.
    • APi: anther collective classification (semi-supervised learning) approach, which iteratively profiles nodes’ labels with APw.
    • APc: a state-of-art method, which profiles users’ attributers based on clustering network.
  • Relationship Type (circle) profiling
    • RPa: profiles friends’ circles based on their attributes.
    • RPn: profiles friends’ circles based on network structure
    • RPan: profiles friends’ circles based on network and attributes, but assumes attributes known.
summary we made the following contributions in this problem
Summary: we made the following contributions in this problem
  • We propose a co-profiling approach that jointly profiles users’ attributes and relationship types (circles) in ego networks.
  • We present the discriminative correlation insight to capture the correlation between attributes and social connections.
  • We conduct extensive experiments to evaluate our algorithms on two tasks based on real-world ego networks.