ArnetMiner – Extraction and Mining of Academic Social Networks 1Jie Tang, 1Jing Zhang, 1Limin Yao, 1Juanzi Li, 2Li Zhang, and 2Zhong Su 1Knowledge Engineering Group, Dept. of Computer Science and Technology Tsinghua University 2IBM, China Research Lab August 25th 2008 1 Motivation “The information need is not only about publication…” “Academic search is treated as document search, but ignore semantics” 2 Examples – Expertise search • When starting a work in a new research topic; • Or brainstorming for novel ideas. Researcher A • Who are experts in this field? • What are the top conferences in the field? • What are the best papers? • What are the top research labs? 3 Examples – Citation network analysis Researcher B • an in-depth understanding of the research field? An Inverted Index Implementation Introduction of Modern Information Retrieval Topics Filtered Document Retrieval with Frequency-Sorted Indexes Parameterised Compression for Sparse Bitmaps Memory Efficient Ranking Topic 31: Ranking and Inverted Index Topic 1 : Theory Topic 27: Information retrieval Topic 23: Index method Signature les: An access Method for Documents and its Analytical Performance Evaluation Topic 21: Framework Topic 34: Parallel computing Self-Indexing Inverted Files for Fast Text Retrieval Topic 22: Compression A Document-centric Approach to Static Index Pruning in Text Retrieval Systems Other Vector-space Ranking with Effective Early Termination Citation Relationship Type Efficient Document Retrieval in Main Memory 4 Static Index Pruning for Information Retrieval Systems Basic theory Comparable work Other Examples – Conference Suggestion authors Which conference should we submit the paper? Researcher C content 5 Examples – Reviewer Suggestion KDD Committee conference Paper content 6 Who are best matching reviewers for each paper? Topic Browser 7 2 1 8 Academic Network Extraction in ArnetMiner = Researcher Profiling + Name Disambiguation 9 Motivating Example Ruud Bolle 2 Office: 1S-D58 Letters: IBM T.J. Watson Information Research Center Contact P.O. Box 704 Ruud Bolle Office: 1S-D58 Yorktown Heights, NY 10598 USA IBM T.J. WatsonCenter Research Center Letters: Packages: IBM T.J. Watson Research Skyline Drive P.O. Box19704 Hawthorne, NY10598 10532USA USA Yorktown Heights, NY Email: Packages: [email protected] T.J. Watson Research Center 19 Skyline Drive Ruud M. Bolle was born in Voorburg, The Netherlands. He received the Bachelor's Hawthorne, NY 10532 USA Degree in Analog Electronics 1977 and the Master's Degree in Electrical Email: [email protected] Engineering in 1980, both from Delft University of Technology, Delft, The In 1983 he received Master'sEducational Degree in Applied Mathematics and in Ruud M.Netherlands. Bolle was born in Voorburg, Thethe Netherlands. He received thehistory Bachelor's the Ph.D. in Electrical Engineering from Brown University, Providence, Rhode Degree 1984 in Analog Electronics in 1977 and the Master's Degree in Electrical Island. In 1984 he from became Research of Staff Member atDelft, the IBM Engineering in 1980, both Delfta University Technology, TheThomas J. Watson Research Center in the Artificial Intelligence Department the Computer Netherlands. In 1983 he received the Master's Degree in Applied of Mathematics andScience in In 1988Engineering he became from manager of University, the newly formed Exploratory 1984 theDepartment. Ph.D. in Electrical Brown Providence, RhodeComputer Vision whichaisResearch part of theStaff Math Sciences Department. Island. In 1984Group he became Member at the IBM Thomas J. Watson Research Center in the Artificial Intelligence Department of the Computer Science Currently, hishe research are onformed video database indexing, video Department. In 1988 becameinterests manager offocused the newly Exploratory Computer processing, visual interaction and biometrics applications. Vision Group which is part human-computer of the Math Sciences Department. video database indexing video processing visual human-computer interaction biometrics applications 1 IBM T.J. Watson Research Center Research Staff Affiliation 2006 Position Homepage Photo Name Ruud Bolle 1984 Sharat Chikkerur, Sharath Pankanti, Alan Jea, Nalini K. Ratha, Ruud M. Bolle: Fingerprint 49 EE Representation Using Localized Texture Features. ICPR (4) 2006: 521-524 2 Andrew Senior, Arun Hampapur, Ying-li Tian, Lisa Brown, Sharath Pankanti, Ruud M. Bolle: 48 EE Appearance models for occlusion handling. Image Vision Comput. 24(11): 1233-1243 (2006) Msuniv Delft University of Technology 47 46 ... 10 1 Bsdate 1977 Bsuniv Delft University of Technology Bsmajor Msmajor Msmajor Electrical Engineering Applied Mathematics Co-author Co-author Publication 2# Publication 1# Title Title Cancelable Biometrics: A Case Study in Venue Fingerprints Date End_page Start_page ICPR 2005 1Ruud M. Bolle, Jonathan H. Connell, Sharath Pankanti, Nalini K. Ratha, Andrew W. Senior: EE The Relation between the ROC Curve and the CMC. AutoID 2005: 15-20 Sharat Chikkerur, Venu Govindaraju, Sharath Pankanti, Ruud M. Bolle, Nalini K. Ratha: EE 2 Novel Approaches for Minutiae Verification in Fingerprint Images. WACV. 2005: 111-116 Ruud Bolle Analog Electronics 1980 1Nalini K. Ratha, Jonathan Connell, Ruud M. Bolle, Sharat Chikkerur: Cancelable Biometrics: 50 EE A Case Study in Fingerprints. ICPR (4) 2006: 370-373 [email protected] Email Phddate Phduniv Phdmajor Msdate Brown University Publications DBLP: Ruud Bolle IBM T.J. Watson Research Center P.O. Box 704 Address Yorktown Heights, NY 10598 USA Address http://researchweb.watson.ibm.com/ ecvg/people/bolle.html Electrical Engineering Ruud M. Bolle interests is a Fellow the IEEE thedatabase AIPR. Heindexing, is Area Editor Currently, his research areoffocused onand video video of Computer Vision andhuman-computer Image Understanding and Associate Editor applications. of Pattern Recognition. Ruud processing, visual interaction and biometrics Academic services M. Bolle is a Member of the IBM Academy of Technology. Ruud M. Bolle is a Fellow of the IEEE and the AIPR. He is Area Editor of Computer Vision and Image Understanding and Associate Editor of Pattern Recognition. Ruud M. Bolle is a Member of the IBM Academy of Technology. IBM T.J. Watson Research Center 19 Skyline Drive Hawthorne, NY 10532 USA Research_Interest 370 Fingerprint Representation Using Localized Texture Features Venue End_page Start_page 2006 2006 521 ICPR 373 coauthor Publication #3 affiliation 524 UIUC Ruud Bolle 2 Publication #5 ... Date coauthor position Professor Motivating Example Ruud Bolle 2 Office: 1S-D58 Letters: IBM T.J. Watson Information Research Center Contact P.O. Box 704 Ruud Bolle Office: 1S-D58 Yorktown Heights, NY 10598 USA IBM T.J. WatsonCenter Research Center Letters: Packages: IBM T.J. Watson Research Skyline Drive P.O. Box19704 Hawthorne, NY10598 10532USA USA Yorktown Heights, NY Email: Packages: [email protected] T.J. Watson Research Center 19 Skyline Drive Ruud M. Bolle was born in Voorburg, The Netherlands. He received the Bachelor's Hawthorne, NY 10532 USA Degree in Analog Electronics 1977 and the Master's Degree in Electrical Email: [email protected] Engineering in 1980, both from Delft University of Technology, Delft, The In 1983 he received Master'sEducational Degree in Applied Mathematics and in Ruud M.Netherlands. Bolle was born in Voorburg, Thethe Netherlands. He received thehistory Bachelor's the Ph.D. in Electrical Engineering from Brown University, Providence, Rhode Degree 1984 in Analog Electronics in 1977 and the Master's Degree in Electrical Island. In 1984 he from became Research of Staff Member atDelft, the IBM Engineering in 1980, both Delfta University Technology, TheThomas J. Watson Research Center in the Artificial Intelligence Department the Computer Netherlands. In 1983 he received the Master's Degree in Applied of Mathematics andScience in In 1988Engineering he became from manager of University, the newly formed Exploratory 1984 theDepartment. Ph.D. in Electrical Brown Providence, RhodeComputer Vision whichaisResearch part of theStaff Math Sciences Department. Island. In 1984Group he became Member at the IBM Thomas J. Watson Research Center in the Artificial Intelligence Department of the Computer Science Currently, hishe research are onformed video database indexing, video Department. In 1988 becameinterests manager offocused the newly Exploratory Computer processing, visual interaction and biometrics applications. Vision Group which is part human-computer of the Math Sciences Department. video database indexing video processing visual human-computer interaction biometrics applications 1 Two key issues: IBM T.J. Watson Research Center Research Staff Affiliation IBM T.J. Watson Research Center P.O. Box 704 Address Yorktown Heights, NY 10598 USA Address http://researchweb.watson.ibm.com/ ecvg/people/bolle.html Position Homepage Photo Name Ruud Bolle 1984 Brown University Analog Electronics 1980 Msuniv Delft University of Technology Msmajor Msmajor Electrical Engineering Applied Mathematics Co-author Co-author Publication 2# Title Title 1Nalini K. Ratha, Jonathan Connell, Ruud M. Bolle, Sharat Chikkerur: Cancelable Biometrics: 50 EE A Case Study in Fingerprints. ICPR (4) 2006: 370-373 Sharat Chikkerur, Sharath Pankanti, Alan Jea, Nalini K. Ratha, Ruud M. Bolle: Fingerprint 49 EE Representation Using Localized Texture Features. ICPR (4) 2006: 521-524 2 Andrew Senior, Arun Hampapur, Ying-li Tian, Lisa Brown, Sharath Pankanti, Ruud M. Bolle: 48 EE Appearance models for occlusion handling. Image Vision Comput. 24(11): 1233-1243 (2006) Cancelable Biometrics: A Case Study in Venue Fingerprints 1Ruud M. Bolle, Jonathan H. Connell, Sharath Pankanti, Nalini K. Ratha, Andrew W. Senior: EE The Relation between the ROC Curve and the CMC. AutoID 2005: 15-20 Sharat Chikkerur, Venu Govindaraju, Sharath Pankanti, Ruud M. Bolle, Nalini K. Ratha: EE 2 Novel Approaches for Minutiae Verification in Fingerprint Images. WACV. 2005: 111-116 ... Date End_page Start_page ICPR 2005 11 Bsdate 1977 Bsuniv Delft University of Technology Bsmajor Publication 1# 2006 46 Ruud Bolle 1 • How to accurately extract the researcher profile information from the Web? Academic services • How to integrate the information from different sources? Publications Ruud M. Bolle interests is a Fellow the IEEE thedatabase AIPR. Heindexing, is Area Editor Currently, his research areoffocused onand video video of Computer Vision andhuman-computer Image Understanding and Associate Editor applications. of Pattern Recognition. Ruud processing, visual interaction and biometrics M. Bolle is a Member of the IBM Academy of Technology. Ruud M. Bolle is a Fellow of the IEEE and the AIPR. He is Area Editor of Computer Vision and Image Understanding and Associate Editor of Pattern Recognition. Ruud M. Bolle is a Member of the IBM Academy of Technology. 47 [email protected] Email Phddate Phduniv Phdmajor Msdate Electrical Engineering DBLP: Ruud Bolle IBM T.J. Watson Research Center 19 Skyline Drive Hawthorne, NY 10532 USA Research_Interest 370 Fingerprint Representation Using Localized Texture Features Venue End_page Start_page 2006 2006 521 ICPR 373 coauthor Publication #3 affiliation 524 UIUC Ruud Bolle 2 Publication #5 ... Date coauthor position Professor Researcher Network Extraction 70.60% of the researchers have at least one homepage/introducing page Research_Interest Fax Affiliation Title Phone Postion Publication_venue Address Person Photo Email Homepage Start_page 71.9% are homepages Publication Name 85.6% from universities Authored Coauthor Researcher End_page Bsdate Bsuniv Phddate Phduniv Phdmajor Msdate Date 40% are in lists and tables 14.4% from companies 28.1% are introducing pages 60% are natural language text Bsmajor Msuniv Msmajor There are a large number of person names having the ambiguity problem 300 most common male names are used by 1 billion+ people (78.74%) in USA Even 3 “Yi Li” graduated from the author’s lab 70% moved at least one time 12 Our Approach Picture – based on Markov Random Field Markov Property: Ya P(Yi | Y j | Y j Yi ) Yb Yc Special cases: P(Yi | Y j | Y j ~ Yi ) - Conditional Random Fields - Hidden Markov Random Fields Ye Yd Yf y4=2 y9=3 t -coauthor y7=2 y1=1 cite coauthor y10=3 y5=2 y6=2 co-conference y3=1 y2=1 coauthor cite coauthor co-conference cite y11=3 y8=1 coauthor x4 x9 x7 x1 Researcher Profiling 13 x5 Name Disambiguation x3 x6 x2 x11 x8 x10 CRFs - Green nodes are hidden vars, - Purple nodes are observations … … … ADR … ADR AFF AFF AFF AFF AFF AFF POS POS POS POS POS POS OTH OTH OTH OTH OTH OTH He is a Professor at 1 p ( y | x) exp j t j (e, y |e , x) k sk (v, y |v , x) Z ( x) vV ,k eE , j 14 UIUC Token Definitions Standard word Special word Including several general ‘special words’ e.g. email address, IP address, URL, date, number, money, percentage, unnecessary tokens (e.g. ‘===’ and ‘###’), etc. Image token <IMAGE src="defaul3.jpg" alt=""/> Term Punctuation marks 15 Words in natural language base NP, like “Computer Science” Including period, question mark, and exclamation mark Feature Definition • Content features Word features Morphological features Image size Image height/width ratio Image format Image color Face recognition The value of height/width. The value of a person photo is often larger than 1 JPG or BMP The number of the “unique color” used in the image and the number of bits used for per pixel, i.e. 32,24,16,8,1 Whether the current image contains a person face Image filename Whether the filename contains (partially) the researcher name Image “ALT” Whether the “alt” of the image contains (partially) the researcher name Image positive keywords Image negative keywords 16 Standard Word Whether the current token is a word Whether the word is capitalized Image Token The size of the image “myself”, “biology” “ads”, “banner”, “logo” Profiling Experiments • Dataset – 1,000 researchers from ArnetMiner.org • Baseline – Amilcare – Support Vector Machines – Unified_NT (CRFs without transition features) • Evaluation measures – Precision, Recall, F1 17 Profiling Results—5-fold cross validation 18 Profiling Task Unified Unified_NT SVM Amilcare Photo 89.11 88.64 88.86 31.62 Position 69.44 64.70 64.68 56.48 Affiliation 83.52 72.16 73.86 46.65 Phone 91.10 78.72 79.71 83.33 Fax 90.83 64.28 64.17 86.88 Email 80.35 75.47 79.37 78.70 Address 86.34 75.15 77.04 66.24 Bsuniv 67.38 57.56 59.54 47.17 Bsmajor 64.20 59.18 60.75 58.67 Bsdate 53.49 40.59 28.49 52.34 Msuniv 57.55 47.49 49.78 45.00 Msmajor 63.35 61.92 62.10 57.14 Msdate 48.96 41.27 30.07 56.00 Phduniv 63.73 53.11 57.01 59.42 Phdmajor 67.92 59.30 59.67 57.93 Phddate 57.75 42.49 41.44 61.19 Overall 83.37 83.37 72.09 73.57 62.30 19 Name Disambiguation Name Affiliation Shanghai Jiao Tong Univ. Yunnan Univ. Tsinghua Univ. Jing Zhang (26) Alabama Univ. Univ. of California, Davis Carnegie Mellon University Henan Institute of Education 20 Proposal of a semi-supervised framework Our Method to Name Disambiguation y4=2 t -coauthor y7=2 y1=1 cite coauthor y10=3 y5=2 y6=2 co-conference y3=1 y2=1 coauthor cite coauthor co-conference cite • A hidden Markov Random Field model y9=3 y11=3 y8=1 coauthor x4 • Hidden Variables Y represent the labels of publications x9 x7 x1 x5 x3 • Observable Variables X represent publications x10 x6 x2 x11 x8 21 • Paper relationships define the dependencies over hidden variables Objective Function maximize P (Y | X ) P (Y ) P ( X | Y ) 1 exp( V (Y )) Z1 1 exp( VNi (Y )) Z1 N i N P( X | Y ) 1 exp( V (i, j )) Z1 i j 1 exp( D( xi , yi )) Z2 xi X 1 exp( D( xi , x j ) I ( yi y j ) [ wk ck ( yi , y j )]) Z1 i j ck C 2 1 2 minimize fobj {D( xi , x j ) I ( yi y j ) [wk ck ( yi , y j )]} D( xi , yi ) log Z i 22 j ck C xi X Relationship Definition C c1 c2 c3 c4 W w1 w2 w3 w4 Relationship Co-Conference CoAuthor Citation Constraints Description pi.pubvenue = pj.pubvenue r, s>0, ai(r)=aj(s) pi cites pj or pj cites pi Feedbacks supplied by users c5 w5 τ-CoAuthor one common author in τ extension p1: A, B, C p2: A, B p3: A, D p4: C, D 23 (0) (3) (2) Mp(1) : p1 p1 1 p2 1 0 p3 01 p2 01 1 01 p3 0 1 1 0 1 Parameterized Distance Function We define the distance function as follows (Basu, 04): D ( xi , x j ) 1 xiT Ax j || xi ||A || x j ||A where || xi ||A xiT Axi 24 We can see that || xi ||A actually maps each vector xi into another new space, i.e. A1/2xi To simplify our question, we define A as a diagonal matrix EM Framework • Initialization • use constraints to generate initial k clusters f obj ( xi , yi ) {D( xi , x j ) I (h l j ) [ wk ck ( pi , p j )]} D ( xi , yi ) • E-Step • M-Step i ck C j x i i :li h • Update cluster centroid y || x || • Update parameter matrix A i i :li h fobj am { i D( xi , x j ) am 25 j D( xi , x j ) am I (li l j ) [wk ck ( pi , p j )]} ck C xim x jm || xi ||A || x j ||A x Ax j T i i A D( xi , yi ) am xi X 2 xim || xi ||2A x 2jm || x j ||2A || xi ||2A || x j ||2A 2 || xi ||A || x j ||A Disambiguation Experiments • Data set: #Public- #Actual ations Person Cheng Chang 12 3 Wen Gao 286 4 Yi Li 42 21 Jie Tang 21 2 Bin Yu 66 12 Abbr. Name 26 Abbr. Name Gang Wu Jing Zhang Kuo Zhang Hui Fang Lei Wang #Public #Actual -ations Person 40 16 54 25 6 2 15 3 109 40 Rakesh Kumar 61 5 Michael Wagner 44 12 Bing Liu 130 11 Jim Smith 33 5 Our Approach vs. Baseline Data Set Person Name Prec. Cheng Chang 100.0 Wen Gao 96.60 Yi Li 86.64 Jie Tang 100.0 Gang Wu 97.54 Jing Zhang 85.0 Kuo Zhang 100.0 Hui Fang 100.0 Real Name Bin Yu 67.22 Lei Wang 68.45 Rakesh 63.36 Kumar Michael 18.35 Wagner Bing Liu 84.88 Jim Smith 92.43 Avg. 82.89 27 Rec. 100.0 62.64 95.12 100.0 97.54 69.86 100.0 100.0 50.25 41.12 F1 100.0 76.00 90.68 100.0 97.54 76.69 100.0 100.0 57.51 51.38 Prec. 100.0 99.29 70.91 100.0 71.86 83.91 100.0 100.0 86.53 88.64 Rec. 100.0 98.59 97.50 100.0 98.36 100.0 100.0 100.0 53.00 89.06 F1 100.0 98.94 82.11 100.0 83.05 91.25 100.0 100.0 65.74 88.85 Our Approach (w/o relation) Prec. Rec. F1 72.73 64.00 68.09 96.17 33.53 49.72 20.97 31.71 25.25 88.68 54.65 67.63 57.69 36.89 45.00 10.79 20.55 14.15 66.67 40.00 50.00 64.71 70.97 67.69 26.50 23.25 24.77 24.13 25.16 24.63 92.41 75.18 99.14 96.91 98.01 67.11 43.04 52.45 60.26 28.13 85.19 76.16 80.42 36.89 50.33 42.57 43.16 86.80 78.51 57.22 89.53 80.64 88.25 95.81 90.68 86.49 93.56 92.12 87.36 94.67 91.39 66.14 60.94 54.29 19.51 59.39 40.93 30.13 60.16 46.67 Baseline (Tan, 2006) Our Approach Contribution of Relationships 100.00 80.00 w/o Relationship 60.00 +CoConference 40.00 +Citation +CoAuthor 20.00 All 0.00 Pre. 29 Rec. F1 Distribution Analysis (1) All methods can achieve good performance (2) Our method can achieve good performance (3) Our method can obtain not bad results, but still need further improvements 30 Modeling the Academic Network and Applications 31 The Academic Network Dr. Tang Association... cite SVM... publish IJCAI write write publish WWW Tree CRF... publish write Limin cite publish Heterogeneous objects: ISWC cite Prof. Wangpublish cite publish write EOS... Semantic... write Annotation... write Pc member write Prof. Li coauthor Write Paper, Person, Conf./Journal Relationships: •Conf./Journal publish paper coauthor Challenges: - How to model the heterogeneous objects in a unified approach? - How to apply the modeling approach to different applications? 32 Academic Network •Paper cite paper •Person write paper •Person is PC member of Conf./Journal •Person is coauthor of person Modeling the Academic Network α β θ Φ A Φ α x z Nd μ T D z c Φ A ad x β θ ad c x T z w Nd w Nd c D D ψ η,σ2 T ACT1 33 θ AC T w Topic β words authors ad α conference ACT2 ACT3 Generative Story of ACT1 Model • Generative process Paper Latent Dirichlet Co-clustering IR NLP ML P(c|z) 1 2 3 4 P(w|z) DM ICDM 0.23 KDD 0.19 …. mining 0.23 clustering 0.19 classification 0.17 …. Shafiei NLP P(c|z) IR DM ML Milios 34 1 2 3 4 P(w|z) ICML 0.23 NIPS 0.19 …. model 0.23 learning 0.19 boost 0.17 …. Shafiei and Milios ICDM NIPS We present a generative model for clustering clusteringdocuments and terms. Our model is a four hierarchical bayesian model. We present efficient inference techniques based on inference Markow Chain Monte Carlo. We report results in document modeling, document and terms clustering … ACT Model 1 Generative process: α β words authors θ Φ A T w ad x z c Nd Topic μ ψ T ACT1 35 D conference ACT Model 2 α Generative process: β authors θ Φ AC T ad x z c w Nd words conference ACT2 36 D ACT Model 3 authors α β θ Φ A ad x Generative process: words T z w Nd c D conference ACT3 37 η,σ2 Applications Association search Expertise search α β θ Φ A T w ad x z c Nd μ D ψ T Researcher interests Hot topic on a conference 38 Topic browser Expertise Search • Calculate the relevance of query q and different objects (i.e., papers, authors, and conferences) • E.g., P (q | d ) wq P ( w | d ) P(w | d ) PLM (w | d ) PACT (w | d ) Nd Nd tf ( w, d ) tf (w, D) PLM ( w | d ) (1 ) Nd Nd Nd D T Ad PACT (w | d ) P(w | z, z ) P( z | x, x ) P( x | d ) z 1 x 1 39 Expertise Search Results Arnetminer data: 14,134 authors 10,716 papers 1,434 confs/journals Evaluation measures: pooled relevance + human judgement Baselines: - Language Model (LM) - LDA - Author Topic (AT) 40 ArnetMiner Today 42 ArnetMiner Today * Arnetminer data: > 0.5 M researcher profiles > 2M papers > 8M citation relationships > 4K conferences * Visits come from more than 165 countries * Continuously +20% increase of visits per month * Currently, more than 1,500 unique-ip visits per day. 43 Top 10 countries 1. USA 6. Canada 2. China 7. Japan 3. Germany 8. France 4. India 9. Taiwan 5. UK 10. Italy Person Search Basic Info. Research Interests Social Network Publications 44 Expertise Search Finding experts, expertise conferences, and expertise papers for “data mining” 45 Association Search Finding associations between persons - high efficiency - Top-K associations Usage: - to find a partner - to find a person with same interests 47 Survey Paper Finding Survey papers 48 Topic Browser 200 topics have been discovered automatically from the academic network 49 Acknowledgements • National Science Foundation of China (NSFC) • National 985 Funding • Chinese Young Faculty Research Funding • Minnesota-China Collaboration Project • IBM CRL • Tsinghua-Google Joint Research Project • National Foundation Science Research (973) 50 Thanks! Q&A & Demo HP: http://keg.cs.tsinghua.edu.cn/persons/tj/ Online URL: http://arnetminer.org If want to know more technique details, please come to our poster session tomorrow night. 51
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