1 Current Research in Data Mining Research Group Current Research in Data Mining Research Group Jiawei Han Data Mining Research Group Department of Computer Science University of Illinois at Urbana-Champaign Acknowledgements: NSF, ARL, NASA, AFOSR (MURI), DHS, Microsoft, IBM, Yahoo!, HP Lab & Boeing June 3, 2016

2 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

Data Mining and Data Warehousing Jiawei Han’s Group at CS, UIUC  Mining patterns and knowledge discovery from massive data  Data mining in heterogeneous information networks  Exploring broad applications of data mining Developed many effective data mining algorithms, e.g., FPgrowth, PrefixSpan, gSpan, StarCubing, CrossMine, RankingCube, CrossClus, RankClus, and NetClus 600+ research papers in conferences and journals Fellow of ACM, Fellow of IEEE, ACM SIGKDD Innovation Award, W. McDowell Award, Daniel Drucker Eminent Faculty Award Textbook, “Data mining: Concepts and Techniques,” adopted worldwide Project lead for NASA EventCube for Aviation Safety [ ] Director of Information Network Academic Research Center funded from Army Research Lab (ARL) [ ] 3

Data Mining Research Group Data Mining Research Group at CS, UIUC 4

New Books on Data Mining & Link Mining 5 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

6 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

Mining Heterogeneous Information Networks RankClus/NetClus RankCompete: A Competing Random Walk Model for Rank-Based Clustering DatabaseData MiningAIIR Top-5 ranked conferenc es VLDBKDDIJCAISIGIR SIGMODSDMAAAIECIR ICDEICDMICMLCIKM PODSPKDDCVPRWWW EDBTPAKDDECMLWSDM Top-5 ranked terms datamininglearningretrieval databasedataknowledgeinformation queryclusteringreasoningweb systemclassificationlogicsearch xmlfrequentcognitiontext RankClass [KDD11] Knowledge Propagation in Heterogeneous Network

8 Similarity Search and Role Discovery in Information Networks Path: ITIPath: ITIGITI Which images are most similar to me in Flickr? PathSim [VLDB11] Meta Path-Guided Similarity Search in Networks A “dirty” Information Network (imaginary) Cleaned/Inferred Adversarial Network Chief Insurgent Cell Lead Automa tically infer Role Discovery in Information Networks [KDD’10] AdviseeTop Ranked Advisor TimeNote David M. Blei 1. Michael I. Jordan PhD advisor, John D. Lafferty Postdoc, 2006 Hong Cheng 1. Qiang Yang02-03 MS advisor, Jiawei Han04-08 PhD advisor, 2008 Sergey Brin 1. Rajeev Motawani Unofficial advisor

Meta-Paths & Their Prediction Power  List all the meta-paths in bibliographic network up to length 4  Investigate their respective power for coauthor relationship prediction  Which meta-path has more prediction power?  How to combine them to achieve the best quality of prediction 9

Relationship Prediction in Heterogeneous Info Networks  Why Prediction of Co-Author Relationship in DBLP?  Prediction of relationships between different types of nodes in heterogeneous networks  E.g., what papers should Faloutsos writes?  Traditional link prediction: homogeneous networks  Co-author networks in DBLP, friendship networks in Facebook  Relationship prediction  Study the roles of topological features in heterogeneous networks in predicting the co-author relationship building  Meta-path guided prediction!  Y. Sun, et al., "Co-Author Relationship Prediction in Heterog. Bibliographic Networks", ASONAM'11, July

Guidance: Meta Path in Bibliographic Network  Relationship prediction: meta path-guided prediction  Meta path relationships among similar typed links share similar semantics and are comparable and inferable 11 papertopic venue author publishpublish -1 mention -1 mention write write -1 contain/contain -1 cite/cite -1  Co-author prediction (A—P—A) using topological features also encoded by meta paths, e.g., citation relations between authors (A—P→P—A)

Case Study in CS Bibliographic Network  The learned significance for each meta path under measure “normalized path count” for HP-3hop dataset 12

Case Study: Predicting Concrete Co-Authors  High quality predictive power for such a difficult task 13  Using data in T0 =[1989; 1995] and T1 = [1996; 2002]  Predict new coauthor relationship in T2 = [2003; 2009]

14 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

Structural Layer: follow the same topology as the document network iTopicModel: Model Set-Up & Objective Function iTopicModel: Model Set-Up & Objective Function  Graphical model: ϴ i =(ϴ i1, ϴ i2,…, ϴ iT ): Topic distribution for document x i Text Layer: follow PLSA, i.e., for each word, pick a topic z~multi(ϴi), then pick a word w~multi(β z )  Objective function: joint probability X: observed text information G: document network Parameters ϴ: topic distribution β: word distribution ϴ is the most critical, need to be consistent with the text as well as the network structure Structure partText part Can model them separately!

Case Study: Topic Hierarchy Building for DBLP

Probabilistic Topic Models with Network-Based Biased Propagation  Text-rich heterogeneous information network  Ubiquitous textual documents (news, papers)  Connect with users and other objects: Topic propagation  Deng, Han et al, “Probabilistic Topic Models with Biased Propagation on Heterogeneous Information Networks”, KDD’11 17  How to discover latent topics and identify clusters of multi-typed objects simultaneously?  How can text data and heterogeneous information network mutually enhance each other in topic modeling and other text mining tasks?

Biased Topic Propagation Intuition:  InfoNet provides valuable information  Different objects have their own inherent information (e.g., D with rich text and U without explicit text)  To treat documents with rich text and other objects without explicit text in a different way Topic(D)  inherent text + connected U Topic(U)  connected D 18 Basic Criterion: (Biased Topic Propagation)  The topic of an object without explicit text depends on the topic of the documents it connects  The topic of a document is correlated with its objects to some extend, and should be principally determined by its inherent content of the text  A simple and unbiased topic propagation does not make much sense

Incorporating Heterogeneous Info. Network 19 L(C): Topic model R(G): Biased propagation

Experiments: DBLP & NSF Awards  Data Collection  DBLP  NSF-Awards  Metrics  Accuracy (AC)  Normalized mutual information (NMI)  Results 20

21 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

Event Cube: An Overview Multidimensional Text Database LAX SJCMIA AUS overshoot undershoot birds turbulence Time Location Topic CA FLTX Location Time Deviation Encounter Topic drill- down roll-up Event Cube Representation Analyst … Multidimensional OLAP, Ranking, Cause Analysis, Topic Summarization/Comparison …… Analysis Support 22 Event Cube: An Organized Approach for Mining and Understanding Anomalous Aviation Events Funded by NASA ( )

Text/Topic Cube: General Idea Heterogeneous: categorical attributes + unstructured text How to combine? Our solution: TimeLocationPlaceEnvironment… Event ReportACN Text data Cube: Categorical Attributes Term/TopicWeight T1W1 T2W2 T3W3 …… Text/Topic Model: Unstructured Text Measure

24 Effective Keyword Search  TopCells (ICDE’ 10): Ranking aggregated cells (objects) in TextCube. Healthcare Reform Healthcare Reform …

Effective OLAP Exploration  TEXplorer (submitted): Integrating keyword-based ranking and OLAP exploration 25 Healthcare Reform Healthcare Reform

Effective Event Tracking  PET (KDD’ 10): tracking popularity and textual representation of events in social communities (twitter) 26 debate, cost, senate, … pass, success, law, … Healthcare Reform Healthcare Reform benefit, profit, effective, …

27 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

Growing Parallel Paths (WWW 2011) Result: 28

Mapping Pages to Records (CIKM’10) Database records can be found on link paths! 29

WinaCS: Web Information Network Analysis for Computer Science Integration of Web structure mining and information network analysis Tim Weninger, Marina Danilevsky, et al., “WinaCS: Construction and Analysis of Web-Based Computer Science Information Networks", ACM SIGMOD'11 (system demo), Athens, Greece, June 2011.WinaCS: Construction and Analysis of Web-Based Computer Science Information Networks 30

31 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

32 Discovery of Swarms and Periodic Patterns in Moving Object Data  A system that mines moving object patterns: Z. Li, et al., “MoveMine: Mining Moving Object Databases", SIGMOD’10 (system demo)  Z. Li, B. Ding, J. Han, and R. Kays, “Mining Hidden Periodic Behaviors for Moving Objects”, KDD’10 (sub)  Z. Li, B. Ding, J. Han, and R. Kays, “Swarm: Mining Relaxed Temporal Moving Object Clusters”, VLDB’10 (sub) ← Bird flying paths shown on Google Earth Mined periodic patterns by our new method → ← Convoy discovers only restricted patterns Swarm discovers more patterns →

GeoTopic Discovery: Mining Spatial Text LDM TDM GeoFolk LGTA Geo-tagged photos w. landscape (coast vs. desert vs. mountain) 33 Z. Yin, et a., GeoTopic Discovery and Comparison, WWW'11

34 Outline  An Introduction to Data Mining Research Group  Mining and OLAPing Information Networks  Mining Heterogeneous Information Networks  Mining Text-Rich Information Networks  OLAPing (Multi-dimensional analysis) of information networks: TextCube, OLAP heterogeneous networks  Taming the Web: WINACS (Integrated mining of Web structures and contents)  Mining Cyber-Physical Systems and Networks  Conclusions

35 Conclusions: Towards Mining Data Semantics in Integrated Heterog. Networks  Most data objects are linked, forming heterogeneous information networks  Most datasets can be “organized” or “transformed” into “structured” multi-typed heterogeneous info. networks  Examples: DBLP, IMDB, Flickr, Google News, Wikipedia, …  Structures can be progressively mined from less organized data sets by info. network analysis  Surprisingly rich knowledge can be mine from such structured heterogeneous info. networks  Clustering, ranking, classification, data cleaning, trust analysis, role discovery, similarity search, relationship prediction, ……  It is promising to mine data semantics from rich info. networks !

References for the Talk  J. Han, Y. Sun, X. Yan, and. S. Yu, “Mining Heterogeneous Information Networks" (tutorial), KDD'10.  Ming Ji, Jiawei Han, and Marina Danilevsky, "Ranking-Based Classification of Heterogeneous Information Networks", KDD'11.  Y. Sun, J. Han, et al., "RankClus: Integrating Clustering with Ranking for Heterogeneous Information Network Analysis", EDBT’09  Y. Sun, Y. Yu, and J. Han, "Ranking-Based Clustering of Heterogeneous Information Networks with Star Network Schema", KDD’09  Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu, “PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks”, VLDB'11  Y. Sun, R. Barber, M. Gupta, C. Aggarwal and J. Han, "Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks", ASONAM'11  C. Wang, J. Han, et al.,,, “Mining Advisor-Advisee Relationships from Research Publication Networks", KDD'10.  Tim Weninger, Marina Danilevsky, et al., “WinaCS: Construction and Analysis of Web- Based Computer Science Information Networks", ACM SIGMOD'11 (system demo)  X. Yin, J. Han, and P. S. Yu, “Truth Discovery with Multiple Conflicting Information Providers on the Web”, IEEE TKDE, 20(6),