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Representation Learning from Graphs: Algorithms and Applications Xiangliang Zhang King Abdullah University of Science and Technology
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Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

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Page 1: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Representation Learning from Graphs: Algorithms and Applications

Xiangliang Zhang

King Abdullah University of Science and Technology

Page 2: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Network Embedding

2

Create features by transforming the graph into a lower dimensional latent representation.

● Node Classification● Link Prediction● Community Detection● Visualization ● etc

Page 3: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Visual Example

3

On Zachary’s Karate Graph:

Page 4: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

What has been done

4

Before 2014

A M

Too expensive

Page 5: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

What has been done

5

Before 2014

A M

After 2014Inspired by Word2Vec’13

DeepWalk’14

node word ? sentence

Page 6: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

What has been done

6

Before 2014

A M

After 2014Inspired by Word2Vec’13

DeepWalk’14

node wordsentence

Random Walk Sequence

Page 7: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

What has been done

7

Before 2014

A M

After 2014Inspired by Word2Vec’13

DeepWalk’14

LINE’15 GraRep’15

Node2vec’16 HOPE’16 SDNE’16

Walklets’17 PEUNE’17

AttentionWalk’18 VERSE’18

Splitter’19, etc

Inspired by CNN

Graph Neural NetworkBruna, Zaremba, Szlam, LeCun, ICLR 2014

GCN: Thomas Kipf, Max Welling. ICLR 2017

Page 8: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

What has been done

8

Before 2014

A M

After 2014Inspired by Word2Vec’13

DeepWalk’14

LINE’15 GraRep’15

Node2vec’16 HOPE’16 SDNE’16

Walklets’17 PEUNE’17

AttentionWalk’18 VERSE’18

Splitter’19, etc

Inspired by CNN

GCN’17GraphSAGE’17

GATs’18FastGCN’18LGCN’18etx

GWNN’19APPNP’19SimplyingGCN’19etc

Page 9: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

What has been done by us

9

Algorithms Applications

• Network Co-embedding [1]• Walking with

Reinforcement [2,3]• Node to a mixture of

distribution [4]• Heterogenous graph

embedding [5]

• User Profiling [1,6]• Paper Recommendation

[7]• Dataset Recommendation

[8,9]• Graph Alignment [10,11]

[1] Meng et al, WSDM’19[2] Akujuobi et al. ICDM’19[3] Akujuobi et al. WSDM’20[4] Chen et al. WWW J’19[5] Chen et al. IJCAI’19

[6] Liang et al, KDD’18[7] Zhang et al. IJCAI’18 [8] Altaf et al. ICDM’19[9] Chen et al. ICDM’19[10] Pei et al. WWW’19[11] Pei et al. IJCAI’19

Page 10: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Outline

• Introduction of Network Embedding

• Co-embedding of Attributed Network for User Profiling

• Walking with Reinforcement for Semi-supervised

Embedding learning from Attributed Network

• Applications on Recommendation

• Future work Discussion

Page 11: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

�4 �2 0 2 4 6

�4

�2

0

2

4

6

Michael I. Jordan

Robert Endre Tarjan

NIPS

SIGKDD

STOC

AAAI

SODA

WSDM

FOCS

SIGIR

WWWICML

ICALP

Problem: Attributed Network Co-Embedding

Goal: map nodes and features in the same space

Input: A + XAttributed Network

Output: Node andfeature embedding

Page 12: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

�4 �2 0 2 4 6

�4

�2

0

2

4

6

Michael I. Jordan

Robert Endre Tarjan

NIPS

SIGKDD

STOC

AAAI

SODA

WSDM

FOCS

SIGIR

WWWICML

ICALP

Problem: Attributed Network Co-Embedding

Our Co-embedding Model• Represent both node and feature in the same space

à to quantitatively measure their relationship

• Represent node/attribute by a Gaussian distribution, not a single point

• à to capture the uncertaintyOutput: • Node representation• Attribute representation

Page 13: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Co-Embedding Model for Attributed Network

The proposed model based on VAE

Inference model Generative model Reconstructed graphG

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Input graph

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ZVi

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Z. Meng, S. Liang, H. Bao, X. Zhang. “Co-Embedding Attributed Networks”. WSDM 2019

Input Encoder Decoder

Reconstructed !𝑨 and !Xshould be close to theinput A and X

Page 14: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Experimental Evaluation, datasets

Applications:• Node classification• Link Prediction

• Attribute Inference• User Profiling

Citation

Social

Co-author

Page 15: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Experimental Evaluation, results

Link Prediction

Attribute Inference

Page 16: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

�4 �2 0 2 4 6

�4

�2

0

2

4

6

Michael I. Jordan

Robert Endre Tarjan

NIPS

SIGKDD

STOC

AAAI

SODA

WSDM

FOCS

SIGIR

WWWICML

ICALP

Experimental Evaluation, results

User Profiling

Machine LearningComputer Vision

Theoretical Computer Science

Page 17: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Outline

• Introduction of Network Embedding

• Co-embedding of Attributed Network for User Profiling

• Walking with Reinforcement for Semi-supervised

Embedding learning from Attributed Network

• Applications on Recommendation

• Future work Discussion

Page 18: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

DeepWalk and its extensions

B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710.

v1

v2

v3 v4

v6

v5

v4 v3 v1 v5 v6

Random walk One example RW path SkipGram with Hierarchical softmax

Hierarchical softmax

Page 19: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Agent of label 1

Agent of label 0

Walking with reinforcement?

• Task: walking to classify nodes• Solution: learn node embedding in semi-supervised way

with reinforcement (earning rewards to classify correctly)

• Starting from the yellow node, with labels {0, 1, 2, 3} • Agent of label 0 visited a number of nodes with label

0 (blue trajectory)• Agent of label 1 visited a number of nodes with label

1 (black trajectory)• NOTE: Agents do not know labels when walking.• Determine where to go by knowing what has been

visited (content of previously visited nodes)

Page 20: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Our proposed Multi-Label-Graph-Walk (MLGW) approach

(a)  

Sigmoid

Concatenation/Summation 

(c)      

  

  

(....)(....)

[.....]

[.....]

[.....]

(....)

(....)

[.....]

[.....]

[.....]

(....)

(....)

(....)

[.....](....)

[.....]

(b) 

+

+

Tanh 

Element-wise product

Summation

(....)

(....)[.....]

[.....]

(... )

[.....]

[.....]

(....)

(....)

[.....]

[.....]

[.....](... )

[.....]

History network

Score network

Classification network

[.....]

[.....]

(... )

[.....](...)/[...] Attributes

U Akujuobi, Y Han, Q Zhang, X Zhang. Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification. ICDM 2019

Decidewhere to go

Memorizewhere I visited

Decidewhere to go

Memorizewhere I visited

Classify

Currentneighbors

Currentneighbors

Page 21: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Evaluation Results

Page 22: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Outline

• Introduction of Network Embedding

• Co-embedding of Attributed Network for User Profiling

• Walking with Reinforcement for Semi-supervised

Embedding learning from Attributed Network

• Applications on Recommendation

• Future work Discussion

Page 23: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Search VS Recommendation

• Search engineshelp you find things you know you are looking

for

• Recommendation engineshelp you find the rest (you may not know but

interesting)

23

Page 24: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

DBLP Records

https://dblp.uni-trier.de/statistics/publicationsperyear

2019

https://dblp.uni-trier.de/statistics/publicationsperyear
Page 25: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

# of records matched "Machine Learning"in DBLP

0

500

1000

1500

2000

2500

3000

3500

4000

4500

20012002

20032004

20052006

20072008

20092010

20112012

20132014

20152016

20172018

2019

Page 26: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Motivations

• Which paper I should read?

• Which benchmark datasets I should use?

26

Page 27: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Paper Recommendation

Given a set of my previously published papers and mypreviously cited papers, rank the top-k interestingpapers, based on a relevance score:

User embeddingvector

Paper embeddingvector

Objective function:

For user u, its score with a related paper vshould be much higher thanits score with unrelated ones v’

Page 28: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Case Study

C.Zhang, L. Yu, X. Zhang, N. Chawla: Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference. IJCAI 2018

Page 29: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Query-based Dataset Recommendation

Page 30: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Our Solution by LearningPaper and Dataset Representation

Input Encoder Decoder

B. Altaf, U. Akujuobi, L. Yu, X. Zhang, Dataset Recommendation via Variational Graph Autoencoder. ICDM 2019.

Page 31: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Outline

• Introduction of Network Embedding

• Co-embedding of Attributed Network for User Profiling

• Walking with Reinforcement for Semi-supervised

Embedding learning from Attributed Network

• Applications on Recommendation

• Future work Discussion

Page 32: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Challenges and future directions

• Incompleteness and noise in graph

• Scalability on big graphs

• Attacks and Robustness of graph embedding

• Other applications, in biology, chemistry, etc

More of our network embedding and network mining work can be found athttps://mine.kaust.edu.sa/Pages/Category_Network.aspx

https://mine.kaust.edu.sa/Pages/Category_Network.aspx
Page 33: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Acknowledgement

My PhD students, visiting students and collaborators

37My group website http://mine.kaust.edu.sa

• Basmah Altaf • Uchenna Akujuob• Shichao Pei• Lu Yu• Qiannan Zhang • Zhuo Yang• Qiang Yang• Hongyan Bao

• Yujun Chen• Zaiqiao Meng• Yufei Han• Chuxu Zhang• Shangsong Liang• Guoxian Yu

http://mine.kaust.edu.sa/
Page 34: Representation Learning from Graphs: Algorithms and ... · B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710. v 1 v

Thank you for your attention!

Lab of Machine Intelligence and kNowledgeEngineering (MINE): http://mine.kaust.edu.sa/

38

http://mine.kaust.edu.sa/

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