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Neural Network-Based Graph-Level Operator Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural Network-Based Graph-Level Operator Learning /Yunsheng Bai.
Author:	Bai, Yunsheng,
Description:	1 electronic resource (169 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Notes:	Advisors: Sun, Yizhou; Wang, Wei Committee members: Chang, Kai-Wei; Gu, Quanquan.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30574631
ISBN:	9798380077835

Neural Network-Based Graph-Level Operator Learning /
Bai, Yunsheng,

Neural Network-Based Graph-Level Operator Learning /Yunsheng Bai. - 1 electronic resource (169 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Graph deep learning models have been popular in graph-based applications such as node classification, link prediction, community detection, etc. The expressive power of deep learning models combined with the increasing amount of graph data enables researchers to solve graph tasks that are traditionally done via algorithmic approaches. For example, neural network-based models have shown state-of-the-art performance on the graph classification task, outperforming kernel-based methods. However, how to perform graph matching related tasks such as Graph Edit Distance (GED) computation, Maximum Common Subgraph (MCS) detection, still requires careful design and utilization of graph deep learning models, due to the unique challenges posed by these NP-hard problems. In this dissertation, six recent researches on neural network-based graph-level operator learning are introduced. Extensive experiments show that the proposed models gain extraordinary improvements compared to the baseline approaches.

English

ISBN: 9798380077835Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

GED computation

Neural Network-Based Graph-Level Operator Learning /
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520 $a Graph deep learning models have been popular in graph-based applications such as node classification, link prediction, community detection, etc. The expressive power of deep learning models combined with the increasing amount of graph data enables researchers to solve graph tasks that are traditionally done via algorithmic approaches. For example, neural network-based models have shown state-of-the-art performance on the graph classification task, outperforming kernel-based methods. However, how to perform graph matching related tasks such as Graph Edit Distance (GED) computation, Maximum Common Subgraph (MCS) detection, still requires careful design and utilization of graph deep learning models, due to the unique challenges posed by these NP-hard problems. In this dissertation, six recent researches on neural network-based graph-level operator learning are introduced. Extensive experiments show that the proposed models gain extraordinary improvements compared to the baseline approaches.
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