1. Collective Network Linkage across Heterogeneous Social Platforms
International Conference on Data Mining, Atlantic City, NJ, USA
Ming Gao
Institute for Data Science and Engineering
East China Normal University, Shanghai, China

2. Our Co-authors Ee-Peng Lim David Lo Feida Zhu
(Singapore Management University)
David Lo
Feida Zhu
Philips Kokoh Prasetyo (Singapore Management University)
Aoying Zhou
(East China Normal University)

3. Roadmap
Related Work
Solution
Empirical Study
Conclusions
Background

4. Background
Social media sites have become extremely popular in recent years
People maintain their accounts and social connections with different social media sites simultaneously
Major applications of network linkage:
Across different social networks
Profile user
Understand user behavior
Recommend users or products across networks …
On a single social network
Detect duplicates from the single network

5. Related Work
Network linkage across different social networks
There is no unsupervised approach which is not domain-specific and can also handle missing and incomplete data. These are the focuses of this work.

6. Network Linkage VS. Record Linkage
Object
Social user
Relational data
Attribute
Heterogeneous
Simple
Implicit and explicit
Explicit
Unfixed
Fixed
Missing data
Many
Few
Relationship
True
False
Challenges
Heterogeneous attributes
Noise or missing data in user attributes
Social connection across heterogeneous networks
Many pairs for
consideration

7. Formulation Two networks: A and B The set of candidate pairs: R
M: matched pairs, U: unmatched pairs
Comparison vector, denoted , represents a set of similarity functions between observed attributes and
Our task is to determine M

8. Overview of Solution Collective network linkage approach (CNL)
Is an unsupervised and probabilistic approach
Integrates heterogeneous attributes
Handles missing data
Evaluates social similarity in a collective manner
Can scale-up to large networks using LSH
Solution
Given a pair of users, denoted ，has similarity vector
Assign a label to in terms of score of the pair which can be computed as

9. Empirical Study Task Datasets Our approach VS. baselines
Self-linking for users from Twitter
Linking users across Foursquare and Twitter
Datasets
Twitter
TWN(x%): size N, noise x%
Foursquare
Ground-truth: 3,534 matched pairs
Our approach VS. baselines
CNL._.: CNLF-E, CNLnonN-E, CNLnonN-G, CNLLF-E
NL ._.: NLnonN-E, NLnonN-G , NLF-E
Mobius

10. Matching Score for Self-linking on TW1109(0%)
NLnonN-E NLnonN-G CNLnonN-G
Distribution assignment is very important
The scores from CNL with correct distribution assignment are more distinguishable than these of NL
CNLnonN-E

11. Compare with Mobius on TWN(10%)
Precision
CNLF-E outperforms Mobius significantly

12. Scalability Test for Self-linking on TWN(10%)
Candidate pairs The elapsed time (Sec.)
Less than 1%
CNLLF-E can scale-up to large networks
Precision Recall

13. Linking Heterogeneous Large Networks
Precision Recall

14. DEMO: Linky http://research.larc.smu.edu.sg/linky/
Linky: Linking networks for unity
Two networks
Foursquare
Twitter
Four Features
Username
Social structure
Temporal features
Content features

15. Conclusions Network linkage across heterogeneous social networks
A unified and unsupervised approach
Integrate heterogeneous user attributes and social connection
Handle missing data
Scale-up to large social networks
Future work
Distributed solution to improve the scalability
Multiple networks, rather than two networks

16. Thank You
for Your Attention

17. Integrates the Heterogeneous Attributes
Attribute similarities can be discrete and continuous values
Exponential family is a set of PDFs or PMFs
Attribute similarities draw from a distribution in exponential family
Log-likelihood
Parameters for mat. & unmat. groups
2-dim. latent vector
may be the missing values
Pr(r \in M)

18. Handles the Missing Values
Performs in an unsupervised manner and handles missing data
It employs EM algorithm to estimate the parameters
In the E-step, it replaces latent variables and missing values to their expectations

19. Scale-up to Large Networks
Speed up the computation via using LSH
LSH on usernames can be utilized to block users
It also reduces the computation of social similarity

20. Links Network in a Collective Manner
CNL works in a collective manner and consists of two stages:
In the first iteration, only non-social attributes are used to link users
Based on the result in the first iteration, then it can link users via integrating social similarity
CNL is terminated if the convergence condition is held