Summarization for Dragon Star Program

(Renmin Univ, Beijing, 5.21~5.27, 2012)

Yueshen Xuxuyueshen@163.com

Zhejiang University

ZJU04/10/23

Overview

Narration What they addressed

Program Profile Knowledge and Expertise

Argumentation What I think over

Research and Research Mode Potpourri

Discussion

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No Dazzle

No Dazzle

Organizer and Lecturer

Organizer Lecturer

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An amiable

lady

CuiPing Li

Jun He

Prof. Qiang Yang, HKUST

• Classification• Transfer Learning

Prof. Jiawei Han, UIUC

• Network Model• Relationship Mining over DBLP

Prof. Liu Huan, ASU

• Online Group Behavior over Social Network

Prof. Jian Pei, SFU

• Mining on Uncertain Data

guest appearance

Curriculum

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Contents Mainly about Data Mining A little about machine learning and database

Base + Advance Base: All should know Advance: Only a few know

Syllabus  Tight and tired

Participation On time, in time and full time

Prof. Liu

6:30

Attention

No comment, no guess, just what it’s what No topics, no transformation and no speculation No detail, just summarization Further study resource repository

http://www.cse.ust.hk/~qyang/2012DStar/ http://www.cs.uiuc.edu/~hanj/dragon12/info12.htm Ask for me Ask for me all is OK

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• What they told me are summarization• Digest not too much• Learn it for needing it

• What you research is to what you meet.

• No qualification

Prof. Yang Classification & Transfer Learning

Classification Decision Trees Neural Networks

Replaced by SVM Bayesian Classifiers

Conditional Independence Naïve Bayesian Network

Support Vector Machines Little about why, mainly about what

Ensemble Classifiers Bagging and Boost (Ada boost) Random Forest

Collaborative Filtering A little

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Prof. Yang, can you speak a little faster?

Just Summarization, little detail

Prof. Yang Classification & Transfer Learning

Transfer Learning What he and his students good at and maybe only good at

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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 do

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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 Learning

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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 Formation

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Prof. Han Information Network Model & Relationship Mining over DBLP

Network Science Plentiful Social network

Social network example Friendship networks vs. blogosphere

Other Network Communication Network Biological Network

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Network model and their representation

Too many, just list some:

• PageRank, Bipartite Networks

Models of Network Formation Explain how social networks

should be organized Model the graph generation

process of social networks Probabilistic Distribution Power Law Long tail law The Erdös-Rényi (ER) Model The Watts and Strogatz Model

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 mine

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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”, VLDB'11

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Prof. Han Information Network Model & Relationship Mining over DBLP

What they take advantage of? Network Schema, called Meta-Path, take an example:

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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’08

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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 references

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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 knowledge

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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 accumulation

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Our Thoughts

How do they do research? Accumulation Real world problem Valuable research problem

Discuss and test to find a suitable method Experiment Paper

Accumulated by means of imitation Not just scan ppt, but do experiments others had did Solve problems others had solved

Different field, different mode Application-Oriented: flexible Method-Oriented: mathematics Topic-Oriented: accumulation

Work as a Team

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Is the problem valuable? Can it be solved by us?

Test again and again. Accumulation, experience, judgment….

Experience and hard work

Revise many times

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.

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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.

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DiscussionDiscussion

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