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Topological graph representation learning on property graph

Version 2 2024-06-06, 00:19
Version 1 2020-09-09, 09:49
conference contribution
posted on 2024-06-06, 00:19 authored by Yishuo ZhangYishuo Zhang, D Gao, AK Cherukuri, L Wang, S Pan, S Li
Property graph representation learning is using the property features from the graph to build the embeddings over the nodes and edges. There are many graph application tasks are using the property graph representation learning as part of the process. However, existing methods on Property graph representation learning ignore either the property features or the global topological structure information. We propose the TPGL, which utilizes the topological data analysis with a bias property graph representation learning strategy. The topological data analysis could augment the global topological information to the embedding and significantly improve the embedding performance on node classification experiments. Moreover, the designed bias strategy aggregated the property features into node embedding by using GNN. Particularly, the proposed TPGL outperformed the start of the art methods including PGE in node classification tasks on public datasets.

History

Volume

12274 LNAI

Pagination

53-64

Location

Hangzhou, China

Start date

2020-08-28

End date

2020-08-30

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030551292

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2020, Springer Nature Switzerland AG

Editor/Contributor(s)

Li G, Shen HT, Yuan Y, Wang X, Liu H, Zhao X

Title of proceedings

KSEM 2020 : Proceedings of Part 1 of the 13th International Conference on Knowledge Science, Enginerring and Management

Event

KSEM Knowledge Science, Engineering and Management. International Conference (13th : 2020 : Hangzhou, China)

Publisher

Springer Cham

Place of publication

Cham, Switzerland

Series

Lecture Notes in Artificial Intelligence (LNAI)