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NS-CUK Seminar: V.T.Hoang, Review on "Heterogeneous Graph Transformer", WWW 2020
1.
Van Thuy Hoang Dept.
of Artificial Intelligence, The Catholic University of Korea hoangvanthuy90@gmail.com
2.
2 Background General GNN
Framework
3.
3 Background Graph Attention
Network
4.
4 Background Relational graph
convolutional networks (R-GCN)
5.
5 Heterogeneous Information Networks (HIN)
In real word, graphs are heterogeneous
6.
6 Open Academic Graph
HTG takes only its one hop edges as inpout without metapath
7.
7 OAG Graph Many
entities
8.
8 Tasks Node Classification
Paper-Field prediction Paper–Field (L1) Paper–Field (L2) Paper-Venue prediction Link prediction Author Disambiguation tasks
9.
9 Heterogeneous Graph v
=< Heterogeneous Graph Transformer > e = <HGT, HAN>
10.
10 Meta Relation
11.
11 Model Heterogeneous Mutual
Attention Heterogeneous Message Passing Target-Specific Aggregation
12.
12 Heterogeneous Mutual Attention
13.
13 Heterogeneous Mutual Attention
ZZZ
14.
14 Heterogeneous Message Passing
15.
15 Target-Specific Aggregation
16.
16 Overall Architecture ZZZ
17.
17 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.
18.
18 Baselines GCN GAT
R-GCN HetGNN (KDD19 Heterogeneous Graph Neural Network) HAN (WWW19 Heterogeneous Graph Attention Network)
19.
19 Results ZZZ
20.
20 ZZZ ZZZ
21.
21 ZZZ ZZZ
22.
22 ZZZ ZZZ
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