Generalizing graph neural network across graphs and time

Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios requi...

Full description

Saved in:
Bibliographic Details
Main Author: WEN, Zhihao
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/7801
https://ink.library.smu.edu.sg/context/sis_research/article/8804/viewcontent/3539597.3572986_pvoa.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-8804
record_format dspace
spelling sg-smu-ink.sis_research-88042023-04-04T03:01:35Z Generalizing graph neural network across graphs and time WEN, Zhihao Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations. 2023-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7801 info:doi/10.1145/3539597.3572986 https://ink.library.smu.edu.sg/context/sis_research/article/8804/viewcontent/3539597.3572986_pvoa.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Graph neural network graph-structured data inductive Databases and Information Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Graph neural network
graph-structured data
inductive
Databases and Information Systems
OS and Networks
spellingShingle Graph neural network
graph-structured data
inductive
Databases and Information Systems
OS and Networks
WEN, Zhihao
Generalizing graph neural network across graphs and time
description Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations.
format text
author WEN, Zhihao
author_facet WEN, Zhihao
author_sort WEN, Zhihao
title Generalizing graph neural network across graphs and time
title_short Generalizing graph neural network across graphs and time
title_full Generalizing graph neural network across graphs and time
title_fullStr Generalizing graph neural network across graphs and time
title_full_unstemmed Generalizing graph neural network across graphs and time
title_sort generalizing graph neural network across graphs and time
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/7801
https://ink.library.smu.edu.sg/context/sis_research/article/8804/viewcontent/3539597.3572986_pvoa.pdf
_version_ 1770576516285988864