On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs-Reference-Cited by-同舟云学术

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

Published:2022-10-17 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Cui Hejie¹,Lu Zijie²,Li Pan³,Yang Carl¹

Affiliation:

1. Emory University, Atlanta, GA, USA

2. University of Illinois at Urbana-Champaign, Urbana, IL, USA

3. Purdue University, West Lafayette, IN, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557661

Reference59 articles.

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2. Davide Bacciu Federico Errica and Alessio Micheli. 2018. Contextual graph markov model: A deep and generative approach to graph processing. In ICML. Davide Bacciu Federico Errica and Alessio Micheli. 2018. Contextual graph markov model: A deep and generative approach to graph processing. In ICML.

3. Protein function prediction via graph kernels

4. The anatomy of a large-scale hypertextual Web search engine

5. Chen Cai and Yusu Wang . 2019 . A simple yet effective baseline for non-attribute graph classification . ICLR Workshop on Representation Learning on Graphs and Manifolds (2019). Chen Cai and Yusu Wang. 2019. A simple yet effective baseline for non-attribute graph classification. ICLR Workshop on Representation Learning on Graphs and Manifolds (2019).

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