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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 Backgrounds Heterogeneous
Information Networks (HIN) and problems Proposed model/ Architecture HGSampling Experiments
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
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