Conference item
The surprising power of graph neural networks with random node initialization
- Abstract:
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Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expr...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 149.9KB, Terms of use)
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- Publisher copy:
- 10.24963/ijcai.2021/291
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Funding
Bibliographic Details
- Publisher:
- International Joint Conferences on Artificial Intelligence Organization
- Host title:
- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, (IJCAI-21)
- Pages:
- 2112-2118
- Publication date:
- 2021-08-09
- Acceptance date:
- 2021-04-29
- Event title:
- 30th International Joint Conference on Artificial Intelligence
- Event location:
- Montreal-themed Virtual Reality
- Event website:
- https://ijcai-21.org/
- Event start date:
- 2021-08-19
- Event end date:
- 2021-08-26
- DOI:
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1187429
- Local pid:
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pubs:1187429
- Deposit date:
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2021-07-24
Terms of use
- Copyright holder:
- IJCAI
- Copyright date:
- 2021
- Rights statement:
- Copyright © 2021, IJCAI
- Notes:
- This paper was presented at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), 19th -26th August, 2021.
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