Mining Social Networks

Ranking, Predicting and Recommending

Tao Zhou

zhutou@ustc.edu

Web Sciences Center, University of Electronic Science and Technology of China

CityU 2013 HK

Content

• Trends

• Personalized Recommendation

• Ranking

• Link Prediction

• Discussion

Social Sciences?

Real Applications?

Trends

• Information Overload: Limited Data -> Big Data

• Personalized Services: Global -> Group -> Individual

• Multi Media: Structured -> Unstructured

• Social Media: Single Networks -> Interacting Networks

Information Overload

Personalized Services

Multi Media

Name: Mark Newman

Sex: Male

Title: Professor

Social Media

Who is who?

Personalized Recommendation

• Information filtering techniques are shifting from finding

out what you want to what you like, from centralized to

decentralized, from population-based to personalized.

• Personalized recommender systems provide a promising

way to solve the information overload problem.

• Personalized recommender systems have already been

successfully applied in many e-commerce web sites,

such as Amazon.com.

Problem Description – The Simplest Version

Personalized recommender systems use the personal information of a user (the historical record of his activities and possibly his profile) to uncover his habits and to consider them in the recommendation.

Known information: the record of interactions between users and objects, the users’ profiles, the objects’ attributes, the content, the time stamps, the user-user relationships, etc.

Required information: whether a target user will like an unselected object, and if so, to what extent he/she likes it. Basically, a personalized recommender system should provide an ordered list of unselected objects to every target user.

Main Methods

• Collaborative filtering

• Iterative refinement

• Diffusion/Local Diffusion

• Principle component analysis

• Latent semantic model

• Content-based analysis

• Latent Dirichlet allocation

• Hybrid algorithm and ensemble approach

• Matrix factorization

• Social Filtering

• ……

Major Challenges • Data Sparsity

• Scalability

• Cold Start

• Diversity vs. Accuracy

• Vulnerability to Attacks

• Value of Time

• Value of User Behavioral Patterns

• Cross-Domain Recommendation

• Social Filtering

• Evaluation of Recommendations

• User Interface

Significance of Diversity and Novelty

VS.

Top 1% Top 3%

Accuracy VS. Information Value

Significance of Diversity and Novelty

Convex Mirror Concave Mirror

VS.

Which one you prefer?

Diversity-Accuracy Dilemma

0 100 200 300 400 500 6000

100

200

300

400

500

600

700

object

# o

f re

com

mended

propose

cos

jaccard

We have tested the standard CF algorithm in MovieLens, and found that only about 15% of objects have the chance to be recommended, and the most frequently recommended one has been recommended to 70% of users.

0 50 100 150 200 250 300 350 400 450 5000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

加边的次数

colla

bora

tive c

luste

ring c

oeff

icie

t

movielens

CF

diffuse

If we assume a small fraction of recommendations will be accepted by the users, then the CF will quickly drive users as a strong convex mirror, namely users will have very similar taste to each other.

Dilemma: Traditional methods usually give accurate yet less diverse recommendations!!

Metrics on Algorithmic Performance

• Accuracy

• ** Overall Ranking: AUC, Ranking Score

• ** Top Recommended Objects: Precision, Recall, F-Measure

• Diversity • ** Intra-Similarity: Recommendations to a user are diverse

• ** Inter-Similarity Recommendations to different users are diverse

• Novelty • ** Popularity

• ** Self-information J. L. Herlocker et al., ACM Trans. Inf. Syst. 22 (2004) 5 T. Zhou et al., EPL 81 (2008) 58004 T. Zhou et al., NJP 11 (2009) 123008 T. Zhou et al., PNAS 107 (2010) 4511

One example: Hybrid Diffusion

T. Zhou et al., PNAS 107 (2010) 4511

Experimental Results

Does Real Social Recommendation Work?

J. Leskovec, et al. ACM Trans. Web 1 (2007) 1 J. Huang, et al. WSDM'2012

Theoretical Model for Social Recommendation

M. Medo, et al., EPL 88 (2009) 38005

T. Zhou, et al.,, PLoS ONE 6 (2011) e20648

G. Cimini, et al., PRE 85 (2012) 046108

A leader-follower network with identical

in-degree is built, where news can only

flow from leaders to followers.

Ranking

Objects Methods / Metrics

Simple Graph Centralities

Directed Graph PageRank, Random Walk

with Restart

Nodes with Mixing Roles HITS

Bipartite Rating Systems Reputation Systems

Collaboration Graph with

Citations

CiteRank, SARA

…… ……

Top Scientists in the Information

Retrieval Area from 1970 to 2008

——According to PageRank and Other Methods

Rank PageRank H-index Betweenness

1 G. Salton E. Garfield G. Salton

2 S. E. Robertson J. D. Ullman A. Gupta

3 S. Abiteboul G. Salton C. Faloutsos

4 N. J. Belkin H. C. Chen (3) L. A. Zadeh

5 C. J. Vanrijsbergen L. A. Zadeh B. Shneiderman

6 Y. Rui B. Shneiderman

(5)

C. T. Yu

7 J. R. Smith J. R. Smith S. Abiteboul

8 T. Saracevic R. Fagin (7) S. K. Chang

9 W. B. Croft T. Kohonen N. Fuhr

10 K. S. Jones S. Lawrence R. Fagin

Ding et al., JASIST 60 (2009) 2229

Ranking Based on the Diffusion of Scientific Credits: A Variant of PageRank

Radicchi et al., PRE 80 (2009) 056103

LeaderRank

23 L. Lü, Y.C. Zhang, C.-H. Yeung, T, Zhou, PLoS ONE 6 (2011) e21202

LocalRank

D.-B. Chen, L. Lü, M.-S. Shang, Y.-C. Zhang, T, Zhou, Physica A 391 (2011) 1777

Building Reputation Systems for

Better Ranking

L.-L. Jiang, et al., arXiv: 1001.2186; Y.-B. Zhou, et al., EPL 94, 48002 (2011).

Link Prediction

• It aims at estimating the existence likelihood of any link.

• It can help in understanding the factors underlying network evolution.

• It can help in evaluating various measurements of node similarity.

• For biological networks, it may reduce the experimental costs.

• For online social networks, it can generate friend recommendations.

• It can be applied in solving the link classification problem in partially labeled networks, improving recommendation for sparse systems, reconstrcucting noisy networks, and so on

Representative Algorithms on Link prediction

• Markov Chains – R. R. Sarukkai, Computer Networks, 33, 377 (2000)

– J. Zhu, J. Hong and J.-G. Hughes, Proceedings of the thirteenth ACM conference on Hypertext and hypermedia (2002)

• Machine Learning – A. Popescul and L. Ungar, in Workshop on Learning Statistical Models from Relational Data, ACM Press,

New York, 2003.

– K. Yu, W. Chu, S. Yu, V. Tresp and Z. Xu, Stochastic Relational Models for Discriminative Link Prediction, in Advance in Neural Information Processing Systems 19, MIT Press, Cambridge, MA, 2007.

• To Predict Based on Node Similarities – D. Liben-Nowell and J. Kleinberg, J. Am. Soc. Inform. Sci. &. Technol. 58, 1019, 2007.

• Collaborative Filtering – Z. Huang, X. Li, H. Chen, Link prediction approach to collaborative filtering, In Proceedings of the 5th

ACM/IEEE-CS Joint Conference on Digital Libraries, ACM Press, New York, 2005.

• Maximum Likelihood Methods – A. Clauset, C. Moore and M. E. J. Newman, Nature 453, 98 (2008)

– S. Redner, Nature 453, 47-48 (1 May 2008)

– R. Guimera, M. Sales-Pardo, PNAS 106, 22073 (2009).

Local Indices Provide as Good Predictions as Global Indices

T. Zhou, L. Lü, Y.-C. Zhang, Eur. Phys. J. B 71 (2009) 623

Network

reconstruction

Network reconstruction

R. Guimera, M. Sales-Pardo, PNAS 106 (2009) 22073 (2009)

Evaluating Network Models

W.-Q. Wang, Q.-M. Zhang, T. Zhou, EPL 98 (2012) 28004

Potential Theory for Directed Networks

Q.-M. Zhang, et al, PLoS ONE 8 (2013) e55437

Potential Theory for Directed Networks

Q.-M. Zhang, et al, PLoS ONE 8 (2013) e55437

Discussion I: Can Social

Sciences Play a Role in

Data Mining?

Anchoring Bias in Online Voting

Z. Yang, Z.-K. Zhang, T. Zhou, EPL 100 (2012) 68002

Improving Recommendation

User-Based CF

Tendency-Based

F. Cacheda et al., ACM Trans. Web 2011

Discussion II: Real

Applications

Opinion Leader Recommendation in Sina Weibo

Personalized Recommendation in E-Commerce

Item-Based Recommendation:

Behavior + Content

Cross-Domain

Recommendation

Furthermore: Personalized

Web Adv., EDM, Mobile Adv.

Data Strategy: An Example

Collaborators Xue-Qi Cheng (ICT,CAS) Giulio Cimini (University of Fribourg) Junming Huang (ICT,CAS) Luo-Luo Jiang (Guangxi Normal University) Zoltan Kuscsik (P. J. Safarik University) Jian-Guo Liu (USST) Linyuan Lv (University of Fribourg) Matus Medo (University of Fribourg) Ming-Sheng Shang (UESTC) Hua-Wei Shen (ICT,CAS) Joesph Wakeling (University of Fribourg) Bing-Hong Wang (USTC) Wen-Qiang Wang (UESTC) Zimo Yang (UESTC) Bill Yeung (University of Fribourg) Wei Zeng (UESTC)

Qian-Ming Zhang (UESTC)

Yi-Cheng Zhang (University of Fribourg) Zi-Ke Zhang (University of Fribourg) Yu-Xiao Zhu (UESTC)

Technical Publications Y.-C. Zhang et al., EPL 80 (2007) 68003 Y.-C. Zhang et al., PRL 99 (2007) 154301 T. Zhou et al., Phys. Rev. E 76 (2007) 046115 T. Zhou et al., EPL 81 (2008) 58004 R. Jie et al., EPL 82 (2008) 58007 T. Zhou et al., EPJB 71 (2009) 623 L. Lv et al., Phys. Rev. E 80 (2009) 046122 T. Zhou et al., New J. Phys. 11 (2009) 123008 M.-S. Shang et al., EPL 88 (2009) 68008 L. Lv, et al., EPL 89 (2010) 18001 T. Zhou et al., PNAS 107 (2010) 4511 M.-S. Shang et al., EPL 90 (2010) 48006 Z.-K.Zhang et al., EPL 92 (2010) 28002 Y.-B. Zhou et al., EPL 94 (2011) 48002 J.-G. Liu et al., PRE 84 (2011) 037101 L. Lv et al., PRE 83 (2011) 066119 T. Zhou et al., PLoS ONE 6 (2011) e20648 L. Lv et al., PLoS ONE 6 (2011) e21202 Z. Liu et al. EPL 96 (2011) 48007 J. Huang, et al. WSDM 2012, p. 573 G. Cimini, et al. Phys. Rev. E 85 (2012) 046108 W.-Q. Wang, et al. EPL 98 (2012) 28004 Z. Yang, et al. EPL 100 (2012) 68002 Q.-M. Zhang et al. PLoS ONE 8 (2013) e55437

References

Review Articles Linyuan Lv, et al., Physica A 390 (2011) 1150 Zi-Ke Zhang, et al., JCST 26 (2011) 767 Linyuan Lv et al., Physics Reports 519 (2012) 1