Towards Inductive and Efficient Explanations for Graph Neural Networks-Reference-Cited by-同舟云学术

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Towards Inductive and Efficient Explanations for Graph Neural Networks

Published:2024-08 Issue:8 Volume:46 Page:5245-5259
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Luo Dongsheng¹^ORCID,Zhao Tianxiang²^ORCID,Cheng Wei³^ORCID,Xu Dongkuan⁴^ORCID,Han Feng⁵^ORCID,Yu Wenchao³^ORCID,Liu Xiao²^ORCID,Chen Haifeng³^ORCID,Zhang Xiang²^ORCID

Affiliation:

1. Florida International University, Miami, FL, USA

2. Pennsylvania State University, State College, PA, USA

3. NEC Lab America, Inc., San Jose, CA, USA

4. North Carolina State University, Raleigh, NC, USA

5. University of California, Berkeley, CA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/34/10582780/10423141-aam.pdf

Reference67 articles.

1. Concrete autoencoders for differentiable feature selection and reconstruction;Abid,2019

2. On differentially private graph sparsification and applications;Arora

3. Protein function prediction via graph kernels

4. Spectral networks and locally connected networks on graphs;Bruna,2013

5. Intelligible Models for HealthCare

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