Summarization for dragon star program


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Summarization for dragon star program

  1. 1. Summarization for Dragon Star Program (Renmin Univ, Beijing, 5.21~5.27, 2012) Yueshen Xu Zhejiang University05/28/12 ZJU
  2. 2. Overview Narration  What they addressed  Program Profile  Knowledge and Expertise Argumentation No  What I think over Dazzle  Research and Research Mode  Potpourri Discussion05/28/12 ZJU
  3. 3. Organizer and Lecturer  Organizer  Lecturer • Classification • Network Model • Transfer • Relationship An Learning Mining overamiable DBLP lady CuiPing Li Prof. Qiang Yang, HKUST Prof. Jiawei Han, UIUC • Online Group • Mining on Behavior over Uncertain Social Network Data guest Prof. Liu Huan, Prof. Jian Pei, SFU Jun He ASU appearanc e 05/28/12 ZJU
  4. 4. Curriculum Contents  Mainly about Data Mining  A little about machine learning and database Base + Advance  Base: All should know  Advance: Only a few know 6:30 Syllabus  Tight and tired Participation Prof. Liu  On time, in time and full time05/28/12 ZJU
  5. 5. Attention • No qualification • What you research is to what you meet. No comment, no guess, just what it’s what No topics, no transformation and no speculation • What they told me are No detail, just summarization summarization Further study resource repository • Digestitnot too muchit • Learn for needing    Ask for me  Ask for me all is OK05/28/12 ZJU
  6. 6. Prof. Yang  Classification & Transfer Learning Classification Prof. Yang, can  Decision Trees you speak a little  Neural Networks faster?  Replaced by SVM  Bayesian Classifiers Just Summarization,  Conditional Independence little detail  Naïve Bayesian Network  Support Vector Machines  Little about why, mainly about what  Ensemble Classifiers  Bagging and Boost (Ada boost)  Random Forest  Collaborative Filtering  A little05/28/12 ZJU
  7. 7. Prof. Yang  Classification & Transfer Learning Transfer Learning  What he and his students good at and maybe only good at05/28/12 ZJU
  8. 8. Prof. Yang  Classification & Transfer Learning I don’t know, but I can bamboozle you  Transfer Learning The ability of a system to recognize and apply knowledge and skills learned in previous tasks to novel tasks or new domains  Easy to talk, hard to do05/28/12 ZJU
  9. 9. Prof. Yang  Classification & Transfer Learning What they focus on  Heterogeneous Transfer Learning  Source-free selection transfer learning  Multi-task transfer learning  Transfer Learning for Link Prediction  EigenTransfer: A Unified Framework for Transfer Learning05/28/12 ZJU
  10. 10. Prof. Han  Information Network Model & Relationship Mining over DBLP An amiable and rigorous old senior  He is involved in the whole process of each paper, ‘Cause he knows details well  He would like to answer every questions  Never acting superior Information Network Model:  Great powers of conception  Fundamental theory of network analysis  Not just about social network. Take a glance at Prof. Han’s contents: ─ Network Science ─ Measure of Metrics of Networks ─ Models of Network Formation05/28/12 ZJU
  11. 11. Prof. Han  Information Network Model & Relationship Mining over DBLP Network Science  Plentiful  Models of Network Formation  Social network  Explain how social networks  Social network example should be organized  Friendship networks vs. blogosphere  Model the graph generation Other Network process of social networks  Communication Network  Probabilistic Distribution  Power Law  Long tail law  Biological Network  The Erdös-Rényi (ER) Model  The Watts and Strogatz Model Network model and their representation Too many, just list some: • PageRank, Bipartite Networks05/28/12 ZJU
  12. 12. Prof. Han  Information Network Model & Relationship Mining over DBLP All based on DBLP  Why? ‘Cause it’s heterogeneous networks  Clustering, Ranking in information networks Problems  What they mine05/28/12 ZJU
  13. 13. Prof. Han  Information Network Model & Relationship Mining over DBLP Classification of information networks  Is VLDB a conference belonging to DB or DM? Similarity Search in information networks  DBLP Who are the most similar to “Christos Faloutsos”?  IMDB Which movies are the most similar to “Little Miss Sunshine”?  E-Commerce Which products are the most similar to “Kindle”? Y. Sun, J. Han, X. Yan, P. S. Yu, and Tianyi Wu, “PathSim: Meta Path-Based Top- K Similarity Search in Heterogeneous Information Networks”, VLDB1105/28/12 ZJU
  14. 14. Prof. Han  Information Network Model & Relationship Mining over DBLP What they take advantage of?  Network Schema, called Meta-Path, take an example:05/28/12 ZJU
  15. 15. Prof. Han  Information Network Model & Relationship Mining over DBLP Relationship Prediction in Information Networks  Whom should I collaborate with?  Which paper should I cite for this topic?  Whom else should I follow on Twitter? Y.Sun, R.Barber, M.Gupta, C.Aggarwal and J.Han. “Co-author Relationship Prediction in Hererogeneous Bibliographic Networks”, ASONAM’11, July 2011 Role Discovery: Extraction Semantic Information from Links Ref. C. Wang, J. Han, et al., “Mining Advisor-Advisee Relationships from Research Publication Networks”, SIGKDD 2010  Data Cleaning and Trust Analysis by InfoNet Analysis Xiaoxin Yin, Jiawei Han, Philip S. Yu, “Truth Discovery with Multiple Conflicting Information Providers on the Web”, TKDE’0805/28/12 ZJU
  16. 16. Prof. Han  Information Network Model & Relationship Mining over DBLP Automatic discovery of Entity Pages  (T. Weinger, Jiawei Han et al. WWW’11)  Given a reference page, can we find entity pages of the same Type? 14 pages references05/28/12 ZJU
  17. 17. Prof. Pei  Uncertain Data Mining Mining uncertain data  Probability is vital  Models and Representation of uncertain data  Mining Frequent Patterns  Classification  Clustering  Outlier Detection Topic-Oriented  Nothing to do with database, namely nothing to do with query  Learn yourself  Outlier Detection on uncertain data is a challenge  This is what I most concern about from point view of knowledge05/28/12 ZJU
  18. 18. Our Thoughts As for pure research, there is no speculation  What’s the proper mode for research?  Method-Oriented: Prof. Yang All about transfer learning All I have to do is solve practical problems with transfer learning, eg. Link predication.  Application-Oriented: Prof. Han Find fun in DBLP, all about relationship mining Every part of Prof. Han’s method is not new, but leading by the problem, the whole framework is innovative  Topic-Oriented: Prof. Pei Clustering and outlier detection on uncertain data He and his team is dependent on solid accumulation05/28/12 ZJU
  19. 19. Our Thoughts Is the problem valuable? Can it be solved by us? How do they do research? Revise many Accumulation  Real world problem  Valuable research problem  times Discuss and test to find a suitable method  Experiment  Paper Accumulated by means of and hard Experience imitation Test again and again. work Accumulation, experience, Not just scan ppt, but do experiments others had did judgment…. Solve problems others had solved Different field, different mode Application-Oriented: flexible Method-Oriented: mathematics Topic-Oriented: accumulation Work as a Team05/28/12 ZJU
  20. 20. Our Thoughts Prof. Pei: Small data  Can you learn a model just with a little data?  Data collection is very costly  Since you can know what you want using 1GB, why do you use 1TB with so many machines?  Prof. Pei: do we really need experiments? No, provided that what you have done is really convictive./ Yes, ‘cause our job is not convictive enough. Read every helpful paper Research should be labeled by researchers, their teams and their labs. Everyone has his own pan, not that all guys just have one.05/28/12 ZJU
  21. 21. Our Thoughts 20/80 Law I have fallen behind from others I had lost myself in clouds of research for one year. I hope I can find my way.05/28/12 ZJU
  22. 22. Discussion05/28/12 ZJU