Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks Yizhou Sun, Rick Barber, Manish Gupta, Charu C. Aggarwal, Jiawei Han 1 Content Background and motivation Problem definition PathPredict: meta path-based relationship
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Presentation Transcript
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Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks Yizhou Sun, Rick Barber, Manish Gupta, Charu C. Aggarwal, Jiawei Han 1<br>
02
Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 2<br>
03
Background Homogeneous networks
One type of objects
One type of links
Examples:
Friendship network in Facebook
Link prediction in homogeneous networks
Predict whether a link between two objects will appear in the future, according to:
Topological feature of the network
Attribute feature of the objects (usually cannot be fully obtained) 3<br>
04
Motivation In reality, heterogeneous networks are ubiquitous
Multiple types of objects
Multiple types of links
Examples:
bibliographic network
movie network
From link prediction to relationship prediction
A relationship between two objects could be a composition of two or more links
E.g., two authors have a co-author relationship if and only if they have co-written a paper
Need to re-design topological features in heterogeneous information network
Our goal:
Study the topological features in heterogeneous networks in predicting the co-author relationship building 4<br>
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Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 5<br>
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Heterogeneous Information Networks 6<br>
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The DBLP Bibliographic Network The underneath meta structure is the network schema: 7<br>
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Co-author Relationship Prediction 8<br>
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Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 9<br>
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The PathPredict Model PathPredict: meta path-based relationship prediction model
Propose meta path-based topological features in HIN
Topological features used in homogeneous networks cannot be directly used
Using logistic regression-based supervised learning methods to learn the coefficients associated with each feature 10<br>
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Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 11<br>
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Existing Topological Measure in Homogeneous Networks 12<br>
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Meta Path-based Topological Features 13<br>
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Meta Paths for Co-authorship Prediction in DBLP 14 List of all the meta paths between authors under length 4<br>
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Four Meta Path-based Measures 15<br>
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Example A-P-V-P-A meta path
PC(J,M) = 7
NPC(J,M) = (7+7)/(7+9)
RW(J,M) = ½
SRW(J,M) = ½ + 1/16 16<br>
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Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 17<br>
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Supervised Learning Framework Training:
T0-T1 time framework
T0: feature collection (x)
T1: label of relationship collection (y)
Testing:
T0’-T1’ time framework, which may have a shift of time compared with training stage 18<br>
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Prediction Model 19<br>
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Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 20<br>
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Experiment Setting 21<br>
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Homogeneous measures vs. heterogeneous measures Homogeneous measures:
Only consider co-author sub-network : common neighbor; rooted PageRank
Consider the whole network and mix all types together: total path count
Heterogeneous measure: Heterogeneous path count Heterogeneous Path Count
produces the best accuracy! 22<br>
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Over Four Datasets 23<br>
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Compare among Different Heterogeneous Measures Normalized path count is slightly better and the hybrid measure that combines all measures is the best 24<br>
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Over four datasets 25<br>
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Impacts of Collaboration Frequency on Different Measures Symmetric random walk is better for predicting frequent co-author relationship 26<br>
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Model Generalization Over Time Conclusion: Historical training can help predict future relationship building 27<br>
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Learned Significance for Each Topological Feature The co-attending venues and the shared co-authors are very critical in determining two authors’ future collaboration 28<br>
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29 Case Studies for Queries<br>
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Content Background and motivation
Problem definition
PathPredict: meta path-based relationship prediction model
Meta path-based topological features
The supervised learning framework and model
Experiments
Conclusions 30<br>
31
Conclusions Problem:
Extend link prediction problem in homogeneous networks into relationship prediction in HIN, using co-authorship prediction as a case study
Solution
Propose meta path-based topological features and measures in HIN
Using logistic regression-based supervised learning methods to learn the coefficients associated with each feature
Results
Hetero. measures beats homo. measures
Hybrid measure beats single measures 31<br>
