BibSLEIGH — Huan

Travelled to:

1 × Canada
1 × Chile
1 × Cyprus
1 × Italy
1 × Taiwan
1 × United Kingdom
16 × USA
2 × Australia
2 × France
2 × Switzerland
4 × China

Collaborated with:

J.Tang X.Hu L.Tang L.Yu H.Gao F.Morstatter Z.Zhao S.Kumar R.Zafarani R.Setiono N.A.Syed K.K.Sung M.A.Abbasi P.Gundecha J.Zhang J.Ye R.Maciejewski ∅ S.Wee S.K.Singhi R.Jin J.Tang X.Wang W.Fan G.Barbier H.Motoda D.P.Makawita K.Tan K.Ng H.Kwah H.Lu J.Pfeffer K.Mayer J.Sampson Y.Chang S.Ranganath Z.Feng A.D.Sarma Z.Nazeri J.Chen G.P.C.Fung J.X.Yu P.S.Yu G.Marshall U.Nambiar J.Wang Y.Chang Liqing Li L.Li Zhenghua Huang Miaoxin Tan R.Cao J.Wu K.Chen T.Wu J.Li R.Patel M.Bae R.Janardan G.E.Alexander E.Reiman

Talks about:

social (13) data (13) select (9) featur (9) network (8) learn (7) media (6) base (6) use (6) locat (5)

Person: Huan Liu

DBLP: Liu:Huan

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2004

2003

2002

2000

1999

1998

1996

1995

2017

Wrote 47 papers:

HT-2015-MorstatterPML #statistics #topic: Text, Topics, and Turkers: A Consensus Measure for Statistical Topics (FM, JP, KM, HL), pp. 123–131.
HT-2015-SampsonMML #clustering #keyword #twitter: Surpassing the Limit: Keyword Clustering to Improve Twitter Sample Coverage (JS, FM, RM, HL), pp. 237–245.
CIKM-2014-TangHCL #interactive #predict: Predictability of Distrust with Interaction Data (JT, XH, YC, HL), pp. 181–190.
HT-2014-AbbasiTL #learning #scalability #using: Scalable learning of users’ preferences using networked data (MAA, JT, HL), pp. 4–12.
HT-2014-AbbasiZTL #network #social: Am i more similar to my followers or followees?: analyzing homophily effect in directed social networks (MAA, RZ, JT, HL), pp. 200–205.
HT-2014-KumarHL #approach #behaviour #identification #twitter: A behavior analytics approach to identifying tweets from crisis regions (SK, XH, HL), pp. 255–260.
HT-2014-TangHL #social #social media #trust: Is distrust the negation of trust?: the value of distrust in social media (JT, XH, HL), pp. 148–157.
KDD-2014-TangTL #future of #recommendation #social #social media: Recommendation in social media: recent advances and new frontiers (JT, JT, HL), p. 1977.
RecSys-2014-GaoTL #network #personalisation #recommendation #social: Personalized location recommendation on location-based social networks (HG, JT, HL), pp. 399–400.
SIGIR-2014-HuTL #detection #microblog: Leveraging knowledge across media for spammer detection in microblogging (XH, JT, HL), pp. 547–556.
HT-2013-KumarMZL #identification: Whom should I follow?: identifying relevant users during crises (SK, FM, RZ, HL), pp. 139–147.
KDD-2013-GundechaRFL #social #social media: A tool for collecting provenance data in social media (PG, SR, ZF, HL), pp. 1462–1465.
KDD-2013-MorstatterKLM #comprehension #twitter: Understanding Twitter data with TweetXplorer (FM, SK, HL, RM), pp. 1482–1485.
KDD-2013-ZafaraniL #approach #behaviour #social #social media: Connecting users across social media sites: a behavioral-modeling approach (RZ, HL), pp. 41–49.
RecSys-2013-GaoTHL #network #recommendation #social: Exploring temporal effects for location recommendation on location-based social networks (HG, JT, XH, HL), pp. 93–100.
RecSys-2013-TangGHL #overview #predict #rating: Context-aware review helpfulness rating prediction (JT, HG, XH, HL), pp. 1–8.
CIKM-2012-GaoTL #correlation #modelling #named #network #social: gSCorr: modeling geo-social correlations for new check-ins on location-based social networks (HG, JT, HL), pp. 1582–1586.
KDD-2012-KumarMMLN #navigation #twitter: Navigating information facets on twitter (NIF-T) (SK, FM, GM, HL, UN), pp. 1548–1551.
KDD-2012-TangL #feature model #social #social media: Unsupervised feature selection for linked social media data (JT, HL), pp. 904–912.
KDD-2012-TangLGS #comprehension #evolution #named #online #trust: eTrust: understanding trust evolution in an online world (JT, HG, HL, ADS), pp. 253–261.
CIKM-2011-HuTL #microblog #semantics #using: Enhancing accessibility of microblogging messages using semantic knowledge (XH, LT, HL), pp. 2465–2468.
CIKM-2011-WangLF #network: Connecting users with similar interests via tag network inference (XW, HL, WF), pp. 1019–1024.
KDD-2011-GundechaBL #network #privacy #social: Exploiting vulnerability to secure user privacy on a social networking site (PG, GB, HL), pp. 511–519.
SAC-2011-Liu #agile #configuration management #using: Rapid application configuration in Amazon cloud using configurable virtual appliances (HL), pp. 147–154.
SAC-2010-WeeL #using: Client-side load balancer using cloud (SW, HL), pp. 399–405.
CIKM-2009-TangL #behaviour #learning #scalability #social: Scalable learning of collective behavior based on sparse social dimensions (LT, HL), pp. 1107–1116.
KDD-2009-TangL #learning #relational #social: Relational learning via latent social dimensions (LT, HL), pp. 817–826.
KDD-2008-TangLZN #community #evolution #multi #network: Community evolution in dynamic multi-mode networks (LT, HL, JZ, ZN), pp. 677–685.
KDD-2008-YeCWLZPBJLAR #data fusion #semistructured data: Heterogeneous data fusion for alzheimer’s disease study (JY, KC, TW, JL, ZZ, RP, MB, RJ, HL, GEA, ER), pp. 1025–1033.
KDD-2008-ZhaoWLYC #data flow #identification #multi #semistructured data: Identifying biologically relevant genes via multiple heterogeneous data sources (ZZ, JW, HL, JY, YC), pp. 839–847.
ICML-2007-ZhaoL #feature model #learning: Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
KDD-2007-ChenZYL #adaptation #clustering #distance #learning #metric: Nonlinear adaptive distance metric learning for clustering (JC, ZZ, JY, HL), pp. 123–132.
KDD-2007-FungYLY: Time-dependent event hierarchy construction (GPCF, JXY, HL, PSY), pp. 300–309.
ICML-2006-SinghiL #bias #classification #learning #set: Feature subset selection bias for classification learning (SKS, HL), pp. 849–856.
KDD-2006-TangZL #classification #data-driven #semantics #taxonomy: Acclimatizing taxonomic semantics for hierarchical content classification from semantics to data-driven taxonomy (LT, JZ, HL), pp. 384–393.
ICML-2004-JinL #induction #robust: Robust feature induction for support vector machines (RJ, HL).
KDD-2004-YuL #array #feature model: Redundancy based feature selection for microarray data (LY, HL), pp. 737–742.
ICML-2003-YuL #feature model #performance: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (LY, HL), pp. 856–863.
KDD-2003-YuL: Efficiently handling feature redundancy in high-dimensional data (LY, HL), pp. 685–690.
ICML-2002-LiuMY #feature model: Feature Selection with Selective Sampling (HL, HM, LY), pp. 395–402.
CIKM-2000-MakawitaTL #database #using: Sampling from Databases Using B+-Trees (DPM, KLT, HL), pp. 158–164.
KDD-1999-SyedLS #case study #independence: A Study of Support Vectors on Model Independent Example Selection (NAS, HL, KKS), pp. 272–276.
KDD-1999-SyedLS99a #concept #incremental #learning: Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
SIGMOD-1998-NgLK #data mining #mining: A Data Mining Application: Customes Retention at the Port of Singapore Authority (PSA) (KN, HL, HK), pp. 522–525.
ICML-1996-LiuS #approach #feature model #probability: A Probabilistic Approach to Feature Selection — A Filter Solution (HL, RS), pp. 319–327.
VLDB-1995-LuSL #approach #data mining #mining #named: NeuroRule: A Connectionist Approach to Data Mining (HL, RS, HL), pp. 478–489.
CASE-2017-LiLHTCLW #analysis #using: The mechanism analysis of bipolar vessel sealing in vitro using EDM model (LL, LL, ZH, MT, RC, HL, JW), pp. 813–818.