LINK PREDICTION-ENABLED
NETWORK COMPLETION
Presenter: Cong Tran
Supervisor: Prof. Won-Yong Shin
Yonsei University
CONTENTS
Introduction
 Network analysis
 Partially observable networks
 Network completion problem
Related work
 DeepNC
Proposed method
 DeepNC (link prediction-enabled)
Experimental evaluation
 Experimental setup
 Performance metric
 Results
Conclusion and future work
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NETWORK MODELING
Biology
Information and technology
Network
Source of pictures:
http://jonlieffmd.com/blog/how-many-different-kinds-of-neurons-are-there
http://jamsessiontopics.blogspot.kr/2014/08/social-networking.html
http://managementlearner.com/law-and-society/
society
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Adjacency matrix (A)
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PARTIALLY OBSERVABLE
NETWORKS
Acquiring a large amount of network
data is often expensive and/or hard
Even when your data is complete, you
may not have the computational resources
to examine all of the data
Partially observable networks
Both nodes and edges are missing
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Picture: http://support.gnip.com/apis/firehose/
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PARTIALLY OBSERVABLE NETWORKS
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NETWORK COMPLETION
Community detection
Influence
maximization
Network completion
Downstream
machine
learning
tasks
Influence
Multi-label graph
classification
𝐍𝐇𝟐
Breast Cancer
Lung Cancer
Melanoma
…
𝐎
6/1/2021
𝐍𝐇𝟐
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Leukemia
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RELATED WORK:
NETWORK COMPLETION PROBLEM
Observable
network
Complete
Network
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There are 2 missing nodes, how to connect them?
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RELATED WORK:
DEEP GENERATIVE MODEL OF GRAPHS
Model and efficiently sample complex distributions over graphs
Learn generative latent variables from observed set of graphs
After learning, the model can generate graphs having similar properties based on learned
generative parameters
Graphs
with similar properties
Learn
Generative
parameters Θ
Deep generative model of graphs
Generate
RELATED WORK: DEEPNC
Cong Tran, Won-Yong Shin, Andreas Spitz, and Michael Gertz. "DeepNC: Deep Generative Network Completion”.
Submitted to TPAMI (in revision).
Facebook in Vietnam
https://arxiv.org/abs/1907.07381
Partial
observation
Facebook in Korea
Privacy
issues
DeepNC
Deep generative
model of graphs
Learn
Generative
parameter Θ
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RELATED WORK:
𝐺1
Generative
parameter Θ
1
DEEPNC
𝐺
𝑃 𝐺1 𝐺𝑂 , Θ
𝑃 𝐺2 𝐺𝑂 , Θ
𝐺𝑂
𝐺2
𝑃 𝐺3 𝐺𝑂 , Θ
Objective function:
𝐺 = argmax 𝑃 𝐺 𝐺𝑂 , Θ
𝐺
𝐺3
1 Cong
Tran, Won-Yong Shin, Andreas Spitz, and Michael Gertz. "DeepNC: Deep Generative Network Completion”
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NETWORK COMPLETION PROBLEM (EXTENDED)
Limitation of DeepNC:
Assume that the observable graph is
complete
=> No missing edge between two
observable nodes
There are 2 missing nodes, how to connect them?
Practical situation
There is a missing edge between two observable nodes,
how to recover it?
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LINK PREDICTION-ENABLED
NETWORK COMPLETION
Facebook in Vietnam
Partial
observation
Facebook in Korea
Privacy
issues
DeepNC
(enhanced)
Deep generative
model of graphs
Learn
Generative
parameter Θ
6/1/2021
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PROPOSED METHOD: DEEPNC (ENHANCED)
1st
iteration
(1)
𝐺𝑂
Network
completion
𝐺 (1)
Update the observable graph
2nd iteration
(2)
𝐺𝑂
Network
completion
𝐺 (2)
⋮
Objective function:
𝐺 = argmax 𝑃 𝐺 𝐺𝑂 , Θ
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𝐺
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EVALUATION
Four datasets: LFR, B-A, CiteSeer, and Protein
 Details of datasets: https://arxiv.org/abs/1907.07381
Node sampling
70%
Ground-truth graph
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Edge sampling
DeepNC
(Enhanced)
90%
Partially observable graph
Compare
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EVALUATION
Performance metric: mean absolute error2 (MAE) is used to measure the difference between ground-truth
graph and recovered graph – the lower, the better
“Similarity” score = Matching score
1
min 𝐀 − 𝐏𝐀𝑷T
𝐏 2
Ground-truth graph (𝐀)
2A
𝑢
𝐏 is the permutation matrix
fast projected fixed-point algorithm for large graph matching, Pattern Recognition, 2016
Recovered graph (𝐀)
EXPERIMENTAL RESULTS
1,6
The MAE of all cases show an
improvement of the enhanced version
over the original DeepNC
- Highest improvement rate (7.6%) can
be seen from Protein dataset
1,2
1
MAE
- The experiment is conducted using only
1 iteration
7.6% gain
1,4
0,8
0,6
0,4
0,2
0
LFR
B-A
DeepNC (original)
6/1/2021
CiteSeer
Protein
DeepNC (enhanced)
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CONCLUSION AND FUTURE WORK
We introduce the partially observable network and our motivation
We propose an enhanced version of DeepNC, where link prediction is enabled
Future work: intensive experiments
6/1/2021
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Email: congtran@ieee.org
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