NS-CUK Seminar: V.T.Hoang, Review on "Gophormer: Ego-Graph Transformer for Node Classification", CoRR 2021

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

2. 2
Contributions
 propose to use Node2Seq that converts graph data to egograph-
based sequential input for transformers
 propose a novel model Gophormer. Gophormer utilizes Node2Seq to
generate input sequential data and encodes it with proximity-
enhanced transformer
 Extensive experiments are conducted on six benchmark datasets.

3. 3
Ego network
 Ego network is a special type of network consisting of one central
node and all other nodes directly connected to it.
 The central node is known as ego, while the other surrounding
nodes directly connected to it are known as alters.

4. 4
Model framework of Gophormer
 ZZZ

5. 5
Node2Seq
 a sampling strategy focusing on high-order neighbors could be a
good choice for heterophilic graph

6. 6
Proximity-Enhanced Transformer
 The proximity-enhanced attention score:
 The proximity encoding is calculated by 𝑀 structural encoding
functions defined as:
 . The proximity encoding functions are defined a

7. 7
Transformer layer

8. 8
EXPERIMENTS
 Datasets
 Cora, Citeseer, Pubmed [17] and DBLP [41]),
 two social network datasets (i.e. Blogcatalog and Flickr [16]).
 Baselines
 GNNs
 GTs

9. 9
Node classification performance

10. 10
CONCLUSION
 Gophormer, which effectively incorporates the structural information
by Node2Seq module and the proximity-enhanced attention
mechanism.
 design consistency regularization loss and multi-sample inference to
alleviate the negative impacts of sampling