The Database and Info. Systems Lab. University of Illinois at Urbana-Champaign 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 Personalized Services 2 and many others. Personalized Search Targeted Advertisement Search Engines Advertisers Richard User College: UIUC Location: Champaign

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? 3

Can we profile users’ missing attributes in social network? 4 Some users provide attributes in their online profiles Some users’ attributes are missing Employer: Yahoo! College: Stanford Employer: ? College:? Employer: Yahoo! College: Berkeley Employer: Twitter College: Berkeley Employer: ? College:? Employer: Twitter College: UIUC Employee: ? College:? Employer: Google College: UIUC Employee: JP Morgan College: UIUC Employer: ? College:?

Thus, we abstract our problem as profiling users' attributes based on friends’ attributes 5 Input: a network G(V, E), some users’ attributes Output: users’ attributes Employer: Yahoo! College: Stanford Employer: Yahoo! College: Berkeley Employer: Twitter College: Berkeley Employer: ? College:? Employer: Twitter College: UIUC Employer: ? College:? Employer: JP Morgan College: UIUC Employer: ? College:? Employer: Yahoo! College: UIUC

While attributes may “propagate” across links— Links are very noisy. 6 Existing methods simply assume that two connected users share the same value for any attribute Employer: Yahoo! College: Stanford Employer: ? College:? Employer: Yahoo! College: Berkeley Employer: Twitter College: Berkeley Employer: ? College:? Employer: Twitter College: UIUC Employer: ? College:? Employer: JP Morgan College: UIUC Employer: ? College:? However, users connect to friends with different values for an attribute Employer: Google College: UIUC 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! 7 Richard and Bob share the same employer, but may have different values for other attributes. Richard and Cindy share the same college, but may have different values for other attributes. Richard and Peter share the same interests, but may have different values for other attributes. Richard Bob Colleagues Cindy Peter College classmates Club friends Users have different types of relationships in real life.

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. 8

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! 9 classmates Employer: Google College: UIUC Employer: Yahoo! College: Berkeley colleagues College: UIUC Employer: Yahoo! Missing

10 But how? Observing how attributes and relationships relate.

Insight: Correlation between attributes and connections through relationship 11 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 Friends do not share all attributes. What attributes they share depend on relationship. 12 The percentages of friends sharing the same value with the ego for different attributes overall of different relationship types.

Observation #2: Connection-Relationship Dependency Friends do not connect to all friends. What friends they connect to depend on relationship. 13 The average connections per user within and across three different relationships types

f 3 = Specifically, we focus on co-profiling upon each user’s ego-network 14 Ego-network : a subnet that around an individual user. Circle1: friends likely to share employee Circle 2: friends likely to share college Circle 3: friends likely to share other attribute Employer: Yahoo! College: Stanford Employer: ? College:? Employer: Yahoo! College: Berkeley Employee: Twitter College: Berkeley Employer: ? College:? Employer: Twitter College: UIUC Employer: ? College:? Employer: Google College: UIUC Employer: Yahoo College: UIUC Attribute Vector f 1 = Circle Assignment x 1 =1 x 3 =1 Association Vector w 1 = w 2 = f 4 = x 4 =2

Solution Overview: we realize co-profiling in an optimization framework 15 Unobserved Friends’ circles Observed User Connections Partially Observed User Attributes Cost Function: capture the 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 16 Attribute-Relationship (circle) Dependency Connection-Relationship Type (circle) Dependency There are other formulas to model the dependencies. However, the function can not be optimized directly, as there are both discrete and continuous variables

Algorithm: we minimize the function via updating each group of variables 17 Update User Attribute Vectors F Update User Circle Assignments X Update Circle Association Vectors W Only propagate values from friends in the same circles Only propagate the attribute value associated with the circle Cosider both user’s attributes and connections Make association vector sparse

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. 18 Ego UsersUsersConnections 17519K110K We share the data online  LinkedinCrawl-Jan LinkedinCrawl-Jan2014 Most users are have three attributes 8K connection are labeled

Experiment: we evaluate our algorithm on both attribute and relationship type profiling Attribute Profiling  AP w : a classic collective classification approach, which profiles a node’s label using weighted votes from its neighbors.  AP i : anther collective classification (semi-supervised learning) approach, which iteratively profiles nodes’ labels with AP w.  AP c : a state-of-art method, which profiles users’ attributers based on clustering network. Relationship Type (circle) profiling  RP a : profiles friends’ circles based on their attributes.  RP n : profiles friends’ circles based on network structure  RP an : profiles friends’ circles based on network and attributes, but assumes attributes known. 19

CP is not only capable of profiling AP and RP and but also outperforms baselines for both 20

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. 21

22 Thank You!