ResearchNet and NewsNet: Two Nets That May Capture Many Lovely Birds

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1 ResearchNet and NewsNet: Two Nets That May Capture Many Lovely Birds Jiawei Han Data Mining Research Group, Computer Science University of Illinois at Urbana-Champaign Acknowledgements: NSF, ARL, NIH, DHS, Microsoft, Yahoo!, LinkedIn, HP Lab March 13, 2015

2 2 Outline Why ResearchNet and NewsNet? What Are the Major Challenges? What Have We Done? Construction of ResearchNet and NewsNet Mining ResearchNet and NewsNet Conclusions

3 Where There Is Information, There Are Networks! Social Networking Websites Biological Network: Protein Interaction 3 Research Collaboration Network Product Recommendation Network via s

4 Evolution: Data Mining Link/Network Mining 4 Han, Kamber and Pei, Data Mining, 3 rd ed Yu, Han and Faloutsos (eds.), Link Mining, 2010 Sun and Han, Mining Heterogeneous Information Networks, 2012

5 The Real World: Heterogeneous Networks Multiple object types and/or multiple link types Movie Studio Venue Paper Author DBLP Bibliographic Network Actor Movie Director The IMDB Movie Network The Facebook Network Homogeneous networks are information loss projection of heterogeneous networks! 5 Directly mining information-richer heterogeneous networks

6 What Are ResearchNet and NewsNet? 6 ResearchNet A generic research network construction, exploration and mining system built for any research domain Research domains: CS, BioMedical, civil engineering, Beyond bibliographic data: DBLP, PubMed, ArXiv, Integration of Webpages, Wikipedia, DBPedia, Freebase,. Need to construct Research-Net by data integration & mining Need to develop rich and powerful search, exploration, and mining functions NewsNet: A news network constructed by data integration and mining to facilitate search, mining and exploration Similar construction, exploration and mining functionalities Integrate KBs + tweets and other kinds of social media

7 7 Why ResearchNet and NewsNet? Data, data everywhere! They are abundant, real datasets! R-Net: DBLP, Webpages, Wikipedia, DBPedia, Freebase,. N-Net: News by different agencies, tweets, blogs, KBs, Tester, tester everywhere! We are domain experts! We are also news experts! We are eager users as well Everyone else can understand it, test it, judge it, and use it! It solves the real problems: Lots of challenging research issues on construction, search and mining, from any angle! A new data-to-network-to-knowledge (D2N2K) paradigm Great collaboration scenarios: Never ending exciting stories!

8 8 Outline Why ResearchNet and NewsNet? What Are the Major Challenges? What Have We Done? Construction of ResearchNet and NewsNet Mining ResearchNet and NewsNet Conclusions

9 Construction of Quality Heterogeneous Networks 9 Entity identification, extraction, clustering, and typing R-Net: DBLP titles, abstraction, contents, Webpages, Wikipedia, DBPedia, Freebase,. N-Net: News, tweets, blogs, KBs, Term and concept hierarchy discovery, concept clustering Role discovery and hierarchy generation Information extraction for special dimensions Time, location, organization, person, event, Data and information integration Truth/quality validation Construction of typed, heterogeneous information networks Incremental update and network maintenance

10 10 Exploring and Mining Heterogeneous Networks Similarity search in heterogeneous information networks Querying any components/types in networks Mining multi-typed heterogeneous networks Clustering, classification, ranking, prediction Information diffusion, community evolution Anomaly Can we really understand what it is talking about? Multi-dimensional data summary Does OLAP make sense? R-net OLAP: On research themes N-Net OLAP: News, tweets, blogs, KBs,

11 11 Outline Why ResearchNet and NewsNet? What Are the Major Challenges? What Have We Done? Construction of ResearchNet and NewsNet Mining ResearchNet and NewsNet Conclusions

12 12 What Has Been Done on Network Mining? Clustering, classification and ranking heterogeneous networks RankClus [EDBT09], NetClus [KDD09], GNetMine [PKDD10], RankClass [KDD11], PathSelClus [KDD12], Similarity search in heterogeneous networks PathSim [VLDB12] Prediction and recommendation in heterogeneous networks PathPredict [ASONAM11], citation prediction [KDD14], personalized recommendation [WSDM14] Evolution and info diffusion in heterogeneous networks [TKDE14] [CIKM14]

13 13 What Can be Mined from Heterogeneous Networks? DBLP: A Computer Science bibliographic database A sample publication record in DBLP (>1.8 M papers, >0.7 M authors, >10 K venues), Knowledge hidden in DBLP Network How are CS research areas structured? Who are the leading researchers on Web search? What are the most essential terms, venues, authors in AI? Who are the peer researchers of Jure Leskovec? Whom will Christos Faloutsos collaborate with? Which types of relationships are most influential for an author to decide her topics? How was the field of Data Mining emerged or evolving? Which authors are rather different from his/her peers in IR? Mining Functions Clustering Ranking Classification + Ranking Similarity Search Relationship Prediction Relation Strength Learning Network Evolution Outlier/anomaly detection

14 14 RankClus, NetClus and RankClass Initialization Randomly partition Repeat Ranking Sub-Network Ranking objects in each sub-network induced from each cluster Generating new measure space SIGMOD VLDB EDBT KDD ICDM SDM AAAI ICML Tom Mary Alice Bob Cindy Tracy Jack Mike Lucy Jim SIGMOD VLDB EDBT AAAI ICML SDM ICDM Ranking KDD Ranking Objects Clustering Estimate mixture model coefficients for each target object Adjusting cluster Until stable

15 Interesting Results from Other Domains RankCompete: Organize your photo album automatically! [WWW 11] Ranking influential treatments for diseases from MEDLINE data 15 Rank treatments for AIDS from MEDLINE [ADC 13]

16 Experiments with Very Small Training Set DBLP: 4-fields data set (DB, DM, AI, IR) forming a heterog. info. network Rank objects within each class (with extremely limited label information) Obtain High classification accuracy and excellent rankings within each class Top-5 ranked conferences Top-5 ranked terms Database Data Mining AI IR VLDB KDD IJCAI SIGIR SIGMOD SDM AAAI ECIR ICDE ICDM ICML CIKM PODS PKDD CVPR WWW EDBT PAKDD ECML WSDM data mining learning retrieval database data knowledge information query clustering reasoning web system classification logic search xml frequent cognition text

17 Some Similarity Measure Is Better Than Others Anhai Doan CS, Wisconsin Database area PhD: 2002 Jignesh Patel CS, Wisconsin Database area PhD: 1998 Meta-Path: Author-Paper-Venue-Paper-Author Amol Deshpande CS, Maryland Database area PhD: 2004 Jun Yang CS, Duke Database area PhD:

18 18 Meta-Path Based Co-authorship Prediction in DBLP Co-authorship prediction problem Whether two authors are going to collaborate for the first time Co-authorship encoded in meta-path Author-Paper-Author Topological features encoded in meta-paths Meta-Path Semantic Meaning Meta-paths between authors under length 4

19 19 The Power of PathPredict Explain the prediction power of each meta-path Wald Test for logistic regression Higher prediction accuracy than using projected homogeneous network 11% higher in prediction accuracy Co-author prediction for Jian Pei: Only 42 among 4809 candidates are true first-time co-authors! (Feature collected in [1996, 2002]; Test period in [2003,2009])

20 20 What Has Been Done on Network Construction? Role discovery Advisor-Advisee [KDD10], Truth validation TruthFinder [KDD07, TKDE08], LTM [VLDB12] Web structure discovery: Growing parallel path [WWW11] Integration of phase mining and topic modeling KERT [SDM 14], CATHY [KDD 13], CATHYHIN [ICDM 13] ToPMine [VLDB 15] Entity recognition and typing in massive text corpora ClusType [KDD 15 submission]

21 21 Role Discovery: Mining Advisor-Advisee Relationships in DBLP Network [C. Wang et al. KDD 10] Propagation of simple, commonly accepted constraints in Time- Constrained Probabilistic Factor Graph (TPFG) Advisor has more publications and longer history than advisee at the time of advising Once an advisee becomes advisor, s/he will not become advisee again Input: Temporal collaboration network 1999 Output: Relationship analysis (0.9, [/, 1998]) Visualized chorological hierarchies Ada 2000 Bob Ada (0.4, [/, 1998]) (0.5, [/, 2000]) 2000 (0.8, [1999,2000]) Ying Smith Jerry Bob (0.7, [2000, 2001]) (0.65, [2002, 2004]) Ying (0.49, [/, 1999]) Jerry (0.2, [2001, 2003]) 2004 Smith

22 22 Role Discovery: Performance & Case Study DBLP data: 654, 628 authors, 1076,946 publications, years provided Labeled data: MathGealogy Project; AI Gealogy Project; Homepage 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% Case study heuristics Supervised learning Empirical parameter Advisee Top Ranked Advisor Time Note optimized parameter David M. Blei 1. Michael I. Jordan PhD advisor, 2004 grad 2. John D. Lafferty Postdoc, 2006 Hong Cheng 1. Qiang Yang MS advisor, Jiawei Han PhD advisor, 2008 Sergey Brin 1. Rajeev Motawani Unofficial advisor

23 23 Enhancing the Quality of Heterogeneous Info. Networks Info. networks could be untrustworthy, error-prone, missing, TruthFinder [KDD 07]: Inference on trustworthiness by mutual enhancement of info provider and statement trustworthiness Latent Truth Model (LTM) [VLDB12]: Modeling two-sided quality to support multiple true values per entity for truth-finding Web sites Facts Objects w 1 f 1 w 2 f 2 w 3 f 3 w 4 f 4 o 1 o 2 Generating Implicit Negative Claims: Positive Claim Negative Claim Correct Claim Incorrect Claim High Precision, High Recall IMDB High Precision, Low Recall Netflix Low Precision, Low BadSour Recall ce Harry Potter

24 24 Truth Discovery: Effectiveness of Latent Truth Model [B. Zhao et al. 2012] Experimental datasets: Large and real Book Authors from abebooks.com (1263 books, 879 sources, claims, 2420 book-author, 100 labeled) Movie Directors from Bing (15073 movies, 12 sources, claims, movie-director, 100 labeled) Effectiveness of Latent Truth Model: Model source quality in other data integration tasks, e.g. entity resolution. Trustworthiness in multi-genre networks (text-rich networks, social networks, etc.)

25 25 Growing Parallel Paths [T. Weninger, WWW 11] Path HTML DIV... DIV DIV... UL LI A B HTML Page B DIV P P LI A X 1 A D HTML DIV UL Page D LI A Y 2 A E LI HTML DIV UL Page E LI A Z A W 3 4 X Y Z W Page A LI A C HTML Page C DIV P A F HTML Page F TABLE TR TD TD A U A V 5 6 U V Example:

26 CATHYHIN: Topic Hierarchy Construction by Integration of Heterogeneous Info. Networks [C. Wang ICDM 13] Using DBLP heterog. Info. network to enhance topical hierarchy generation CATHYHIN output for DBLP data Hierarchies generated not only on topical phrases but also on authors & venues database system query processing concurrency control Divesh Srivastava Surajit Chaudhuri Jeffrey F. Naughton ICDE SIGMOD VLDB information retrieval retrieval question answering W. Bruce Croft James Allan Maarten de Rijke SIGIR ECIR CIKM 26 text categorization text classification document clustering multi-document summarization relevance feedback query expansion collaborative filtering information filtering

27 27 KERT: Topic Modeling + Phase Mining [M. Danilevsky, et al. SDM 14] Run bag-of-words model inference, and assign topic label to each token Extract candidate keyphrases within each topic Frequent pattern mining Rank the keyphrases in each topic Popularity: information retrieval vs. cross-language information retrieval Discriminativeness: only frequent in documents about topic t Concordance: active learning vs. learning classification Completeness: vector machine vs. support vector machine KERT [Danilevsky et al. 14] learning support vector machines reinforcement learning feature selection conditional random fields classification decision trees :

28 28 Phrase Mining: Frequent Pattern Mining + Statistical Analysis [El-Kishky et al., VLDB 15] Quality phrases Significance score [Church et al. 91]: α(a, B) = ( AB A )/ AB [Markov blanket] [feature selection] for [support vector machines] [knowledge discovery] using [least squares] [support vector machine] [classifiers] [support vector] for [machine learning] Phrase Raw freq. True freq. [support vector machine] [vector machine] 95 0 [support vector]

29 29 ToPMine: Experiments on Yelp Reviews

30 30 Mining Quality Phrases from Massive Text Corpora [J. Liu et al. SIGMOD 15] Integrate the segmentation with the phrase quality assessment Only frequent phrases with reasonable quality are considered Phrase quality guides the segmentation, and the segmentation rectifies the phrase quality estimation

31 EventCube, ResearchInsight and NewsNetExplorer [Tao, et al., SIGMOD 13, KDD 13, SIGMOD 14 demos] 31 Several prototyped systems constructed in our research EventCube [Funded by NASA, Han and Zhai groups] ResearchInsight (prototype of ResearchNet) NewsNetExplorer (Prototype of NewsNet)

32 32 Adding New Dimensions: GeoTopic Discovery [Z. Yin et al., WWW 11] LGTA: GeoTopic discovery with geo-tagged photos and associated text LDM Geo-tagged photos w. landscape (coast vs. desert vs. mountain) TDM GeoFolk LGTA

33 33 Outline Why ResearchNet and NewsNet? What Are the Major Challenges? What Have We Done? Construction of ResearchNet and NewsNet Mining ResearchNet and NewsNet Conclusions

34 34 Network Construction: Still Many Challenges Extraction of phrases, entities, types, relationships (using KB) Domain-based and KB-guided concept hierarchy construction Hierarchy enhancement or knowledge enrichment: Given a rough or existing hierarchy, derive a deeper and complete one Mining roles (e.g., advisor) and creating infobox properties Network construction by integrating Web, KB, social network (Facebook, LinkedIn) and media (tweets) Automatic finding most related info on the web Truth consolidation and multi-source data integration Network cube: Build multi-dim. and multi-level info. networks Build sophisticated networks by deepening into contents Never ending network construction: Enrichment by data mining

35 35 A Two-Stage Framework for Concept Hierarchy Construction

36 Distant Supervision: Enabling Structured Analysis of Unstructured Text Corpus [X. Ren et al. KDD 15 sub] Detect entity mentions from text Map candidate mentions to KB entities of target types Use confidently mapped {mention, type} to infer types of remaining candidate mentions 36 Identifying token span as entity mentions in documents and labeling their types Target Types FOOD/ LOCATION JOB_TITLE/ EVENT ORGANIZATION/ The best BBQ I ve tasted in Phoenix! I had the pulled pork sandwich with coleslaw and baked beans for lunch.... The owner is very nice. Plain text The best BBQ:Food I ve tasted in Phoenix:LOC! I had the [pulled pork sandwich]:food with coleslaw:food and [baked beans]:food for lunch. The owner:job_title is very nice. Text with typed entities

37 ClusType: Example Output and Relation Phrase Clusters Extracts more mentions and predicts types with higher accuracy Not only synonymous relation phrases, but also both sparse and frequent relation phrase can be clustered together 37 boosts sparse relation phrases with type information of frequent relation phrases

38 Construction and Enrichment of ResearchNet: Author and Venue Profiling 38 Use author, paper, and venue info. to find relevant web pages from multiple sources, conduct data integration + entity linking Extract interested attribute values for author/venue from the resolved web pages Resolve conflicts and remove redundancy through truth finding.

39 39 Paper Profiling: Modeling Research Paper Contents in Heterog. Networks Paper category identification: survey vs. research vs. demonstration papers Identifying the themes of a paper Identifying the important terms of a paper Distinguish synonyms, assisted terms, major terms Identifying the major methods or algorithms addressed in a research paper Identifying major applications addressed in a research paper Linking terms with other papers to form networks? Automated paper summary? Automated paper review? Missing or recommend related work? Too similar to some existing work?

40 40 Outline Why ResearchNet and NewsNet? What Are the Major Challenges? What Have We Done? Construction of ResearchNet and NewsNet Mining ResearchNet and NewsNet Conclusions

41 41 Exploring and Mining ResearchNet Similarity search/queries on ResearchNet Similar papers, similar venues, similar authors, similar terms, similar entities (algorithms, genes, diseases, treatments) Similar relationships (author-author, author-term, Similar evolution, diffusion, dynamics, Recommendations and prediction of Recommend coauthors, citations, experts, papers, venues, Clustering and ranking Classification, active learning, transfer learning,. Network folding

42 42 Network Folding-Based ResearchNet Summarization Network folding: brings close the node/link pairs that were separated by multiple hops Help answer queries Q1: Show the venuevenue citation network for papers in venue A citing those published in venue B in [ ] Q2: Show the affiliation collaboration network for the papers using RBF kernel tricks

43 43 Effective OLAP Exploration TEXplorer (CIKM 11): Integrating keyword-based ranking and OLAP exploration OLAP query processing: cross network queries, drilling, Healthcare Reform

44 44 Outline Why ResearchNet and NewsNet? What Are the Major Challenges? What Have We Done? Construction of ResearchNet and NewsNet Mining ResearchNet and NewsNet Conclusions

45 45 Conclusions Big data are likely interconnected and implicitly structured Unstructured data are likely convertible to structured networks! Mining big data construction and mining of heterogeneous networks actionable knowledge (The D2N2K paradigm) Surprisingly rich knowledge can be mine from structured heterogeneous info. Networks Quality, structured heterogeneous info. networks could be automatically constructed from massive data ResearchNet and NewsNet Real data, real cases, real testers, real users, and real tools They are really exciting projects and will have real impact!