Advisor-advisee Relationship Mining from Research Publication Network

Chi Wang1, Jiawei Han1, Yuntao Jia1, Jie Tang2, Duo Zhang1, Yintao Yu1, Jingyi Guo2

1 University of Illinois at Urbana-Champaign{chiwang1, hanj, yjia3, dzhang22, yintao}@illinois.edu

2 Tsinghua University {jietang, guojy07@mails}.tsinghua.edu.cn

Motivation

• Latent knowledge in information network: – Relationships:

friends/relatives/colleagues/enemies?• If they can be mined by links, it will benefit our

study in– Community structure clustering & classification– Exerting Searching search & ranking– Evolution patterns prediction &

recommendation

Overall Framework

Overall Framework• ai: author i

• pj: paper j• py: paper year• pn: paper#• sti,yi: starting

time• edi,yi: ending

time• ri,yi: ranking

score

Heuristics

• ASSUMPTION 1: at each time t during the publication history of a node x, x is either being advised or not being advised. Once x starts to advise another node, it will never be advised again.

• ASSUMPTION 2: for a given pair of advisor and advisee, the advisor always has a longer publication history than the advisee.

Stage 1: Preprocessing

• From author-paper bipartite network to authorship collaboration homogenous network.

• Then a filtering process is performed to remove unlikely relations of advisor-advisee.

Stage 1: Preprocessing• Author aj is not considered to be ai’s advisor if

one of the following conditions holds:

Stage 1: Preprocessing

• In addition, estimate:– the starting time stij is estimated as the time they

started to collaborate;– the ending time edij can be estimated as either the

time point when the Kulczynski measure starts to decrease;

– the local likelihood of aj being ai’s advisor lij

Stage 2: Graph Factor Model

• For each node ai, there are three variables to decide: yi, sti, and edi. Suppose we have already had a local feature function g(yi, sti, edi) defined on the three variables of any given node.

Experiment Results• DBLP data: 654, 628 authors, 1076,946

publications, years provided.

Datasets RULE SVM IndMAX TPFG

TEST1 69.9% 73.4% 75.2% 78.9% 80.2% 84.4%

TEST2 69.8% 74.6% 74.6% 79.0% 81.5% 84.3%

TEST3 80.6% 86.7% 83.1% 90.9% 88.8% 91.3%

Empiricalparameter

optimizedparameter

heuristics Supervised learning

Case StudyAdvisee Top Ranked Advisor Time Note

David M. Blei

1. Michael I. Jordan 01-03 PhD advisor, 2004 grad2. John D. Lafferty 05-06 Postdoc, 2006

Hong Cheng

1. Qiang Yang 02-03 MS advisor, 20032. Jiawei Han 04-08 PhD advisor, 2008

Sergey Brin 1. Rajeev Motawani 97-98 “Unofficial advisor”

Effect of rules - ROC curve

• Filtering rules in TPFG

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