1. Van Thuy Hoang
Dept. of Artificial Intelligence,
The Catholic University of Korea
hoangvanthuy90@gmail.com

2. 2
 ZZZ

3. 3
Background
 General GNN Framework

4. 4
Background
 Graph Attention Network

5. 5
Background
 Relational graph convolutional networks (R-GCN)

6. 6
Heterogeneous Information
Networks (HIN)
 In real word, graphs are heterogeneous

7. 7
Open Academic Graph
 HTG takes only its one hop edges as inpout without metapath

8. 8
OAG Graph
 Many entities

9. 9
Tasks
 Node Classification
 Paper-Field prediction
 Paper–Field (L1)
 Paper–Field (L2)
 Paper-Venue prediction
 Link prediction
 Author Disambiguation tasks

10. 10
Heterogeneous Graph
 v =< Heterogeneous Graph Transformer >
 e = <HGT, HAN>

11. 11
Meta Relation

12. 12
Model
 Heterogeneous Mutual Attention
 Heterogeneous Message Passing
 Target-Specific Aggregation

13. 13
Heterogeneous Mutual Attention

14. 14
Heterogeneous Mutual Attention
 ZZZ

15. 15
Heterogeneous Message Passing

16. 16
Target-Specific Aggregation

17. 17
Overall Architecture
 ZZZ

18. 18
HGSampling
 keep a similar number of nodes and edges for each type
 keep the sampled sub-graph dense to minimize the information loss and
reduce the sample variance.

19. 19
Baselines
 GCN
 GAT
 R-GCN
 HetGNN (KDD19 Heterogeneous Graph Neural Network)
 HAN (WWW19 Heterogeneous Graph Attention Network)

20. 20
Results
 ZZZ

21. 21
ZZZ
 ZZZ

22. 22
ZZZ
 ZZZ

23. 23
ZZZ
 ZZZ