BibSLEIGH — Hong

Travelled to:

1 × Australia
1 × Canada
1 × France
1 × Italy
1 × Japan
1 × Korea
1 × Norway
10 × USA
2 × China

Collaborated with:

J.Han J.X.Yu X.Yan D.Xin L.Qin D.Lo L.Chen J.Cheng X.Huang S.Song N.Zheng Y.Zhou W.Tian R.Li Z.Shang P.S.Yu Y.Rong Z.Mo X.Zhang W.Chan C.Sun F.Shang Y.Liu Y.Zhu R.Yu Z.Liu Y.Liu L.Yang J.Yang X.Li D.Surian Y.Tian E.Lim M.Qiao Z.Xu Y.Ke Y.Wang H.Wang X.Wang S.Khoo C.Sun Q.Mei C.Zhai Y.Xu T.Lin W.Lam Z.Zhou A.M.So L.Chang C.Zhang X.Lin Y.Sun T.Wu Z.Yin X.Yin P.Zhao W.Fan K.Zhang J.Gao O.Verscheure

Talks about:

pattern (9) graph (9) mine (8) approach (6) search (6) discrimin (4) network (4) larg (4) base (4) top (4)

Person: Hong Cheng

DBLP: Cheng:Hong

Contributed to:

2015

2014

2013

2012

2009

2008

2006

2005

2004

Wrote 31 papers:

KDD-2015-RongCM #identification #modelling #social #why: Why It Happened: Identifying and Modeling the Reasons of the Happening of Social Events (YR, HC, ZM), pp. 1015–1024.
VLDB-2015-ZhangC0 #approach #distributed #graph #set: Bonding Vertex Sets Over Distributed Graph: A Betweenness Aware Approach (XZ, HC, LC), pp. 1418–1429.
CIKM-2014-ShangLCC #analysis #component #robust: Robust Principal Component Analysis with Missing Data (FS, YL, JC, HC), pp. 1149–1158.
CIKM-2014-XuLLZCS #mining: Latent Aspect Mining via Exploring Sparsity and Intrinsic Information (YX, TL, WL, ZZ, HC, AMCS), pp. 879–888.
SIGMOD-2014-HuangCQTY #community #graph #query #scalability: Querying k-truss community in large and dynamic graphs (XH, HC, LQ, WT, JXY), pp. 1311–1322.
SIGMOD-2014-QinYCCZL #graph #pipes and filters #scalability: Scalable big graph processing in MapReduce (LQ, JXY, LC, HC, CZ, XL), pp. 827–838.
VLDB-2014-Song0C #on the #set: On Concise Set of Relative Candidate Keys (SS, LC, HC), pp. 1179–1190.
VLDB-2014-SongCY0 #constraints: Repairing Vertex Labels under Neighborhood Constraints (SS, HC, JXY, LC), pp. 987–998.
CSMR-2013-SurianTLCL #network #predict: Predicting Project Outcome Leveraging Socio-Technical Network Patterns (DS, YT, DL, HC, EPL), pp. 47–56.
VLDB-2013-HuangCLQY #network #scalability: Top-K Structural Diversity Search in Large Networks (XH, HC, RHL, LQ, JXY), pp. 1618–1629.
VLDB-2013-QiaoQCYT #graph #keyword #scalability: Top-K Nearest Keyword Search on Large Graphs (MQ, LQ, HC, JXY, WT), pp. 901–912.
CIKM-2012-LiYHCS #mvc #network #robust: Measuring robustness of complex networks under MVC attack (RHL, JXY, XH, HC, ZS), pp. 1512–1516.
CIKM-2012-ZhuYCQ #approach #classification #feature model #graph: Graph classification: a diversified discriminative feature selection approach (YZ, JXY, HC, LQ), pp. 205–214.
FSE-2012-ChanCL #api: Searching connected API subgraph via text phrases (WKC, HC, DL), p. 10.
ICPR-2012-ChengYLL #categorisation #kernel #nearest neighbour: A Pyramid Nearest Neighbor Search Kernel for object categorization (HC, RY, ZL, YL), pp. 2809–2812.
SIGMOD-2012-XuKWCC #approach #clustering #graph #modelling: A model-based approach to attributed graph clustering (ZX, YK, YW, HC, JC), pp. 505–516.
VLDB-2012-ChengSCWY #named: K-Reach: Who is in Your Small World (JC, ZS, HC, HW, JXY), pp. 1292–1303.
ISSTA-2009-ChengLZWY #debugging #graph #identification #mining #using: Identifying bug signatures using discriminative graph mining (HC, DL, YZ, XW, XY), pp. 141–152.
KDD-2009-LoCHKS #approach #behaviour #classification #detection #mining: Classification of software behaviors for failure detection: a discriminative pattern mining approach (DL, HC, JH, SCK, CS), pp. 557–566.
VLDB-2009-ZhouCY #clustering #graph: Graph Clustering Based on Structural/Attribute Similarities (YZ, HC, JXY), pp. 718–729.
ICPR-2008-YangYZC #categorisation: Layered object categorization (LY, JY, NZ, HC), pp. 1–4.
KDD-2008-FanZCGYHYV #mining #modelling: Direct mining of discriminative and essential frequent patterns via model-based search tree (WF, KZ, HC, JG, XY, JH, PSY, OV), pp. 230–238.
SIGMOD-2008-SunWYCHYZ #mining #named #network: BibNetMiner: mining bibliographic information networks (YS, TW, ZY, HC, JH, XY, PZ), pp. 1341–1344.
SIGMOD-2008-YanCHY #graph #mining: Mining significant graph patterns by leap search (XY, HC, JH, PSY), pp. 433–444.
ICPR-v1-2006-ChengZS #detection: Boosted Gabor Features Applied to Vehicle Detection (HC, NZ, CS), pp. 662–666.
KDD-2006-MeiXCHZ #analysis #generative #semantics: Generating semantic annotations for frequent patterns with context analysis (QM, DX, HC, JH, CZ), pp. 337–346.
KDD-2006-XinCYH: Extracting redundancy-aware top-k patterns (DX, HC, XY, JH), pp. 444–453.
VLDB-2006-XinHCL #approach #multi #query #ranking: Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach (DX, JH, HC, XL), pp. 463–475.
KDD-2005-YanCHX #approach: Summarizing itemset patterns: a profile-based approach (XY, HC, JH, DX), pp. 314–323.
VLDB-2005-XinHYC #mining #set: Mining Compressed Frequent-Pattern Sets (DX, JH, XY, HC), pp. 709–720.
KDD-2004-ChengYH #database #incremental #mining #named #scalability: IncSpan: incremental mining of sequential patterns in large database (HC, XY, JH), pp. 527–532.