DPGNN: Dual-Perception Graph Neural Network for Representation Learning Article Swipe
Li Zhou
,
Wenyu Chen
,
Dingyi Zeng
,
Shaohuan Cheng
,
Wanlong Liu
,
Malu Zhang
,
Hong Qu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.07869
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.07869
Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2110.07869
- https://arxiv.org/pdf/2110.07869
- OA Status
- green
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3205006230
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- OpenAlex ID
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https://openalex.org/W3205006230Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2110.07869Digital Object Identifier
- Title
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DPGNN: Dual-Perception Graph Neural Network for Representation LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-15Full publication date if available
- Authors
-
Li Zhou, Wenyu Chen, Dingyi Zeng, Shaohuan Cheng, Wanlong Liu, Malu Zhang, Hong QuList of authors in order
- Landing page
-
https://arxiv.org/abs/2110.07869Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2110.07869Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2110.07869Direct OA link when available
- Concepts
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Message passing, Computer science, Graph, Theoretical computer science, Attention network, Node (physics), Artificial intelligence, Distributed computing, Structural engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.remarkable | 13 |
| abstract_inverted_index.structural | 138 |
| abstract_inverted_index.structures | 159 |
| abstract_inverted_index.adaptively. | 131 |
| abstract_inverted_index.demonstrate | 160 |
| abstract_inverted_index.graph-based | 17 |
| abstract_inverted_index.information | 39, 130, 140, 144 |
| abstract_inverted_index.instantiate | 107 |
| abstract_inverted_index.iteratively | 36 |
| abstract_inverted_index.multi-space | 99 |
| abstract_inverted_index.outperforms | 164 |
| abstract_inverted_index.performance | 14 |
| abstract_inverted_index.restriction | 72 |
| abstract_inverted_index.superiority | 172 |
| abstract_inverted_index.topological | 158 |
| abstract_inverted_index.versatility | 174 |
| abstract_inverted_index.Experimental | 150 |
| abstract_inverted_index.discrepancy, | 70 |
| abstract_inverted_index.interaction. | 101 |
| abstract_inverted_index.neighborhood | 38, 129, 139 |
| abstract_inverted_index.node-to-step | 122 |
| abstract_inverted_index.inflexibility | 60 |
| abstract_inverted_index.node-specific | 95, 127, 187 |
| abstract_inverted_index.representation | 148 |
| abstract_inverted_index.simultaneously | 145 |
| abstract_inverted_index.Dual-Perception | 114 |
| abstract_inverted_index.feature-related | 143 |
| abstract_inverted_index.message-passing | 33, 85, 110 |
| abstract_inverted_index.semi-supervised | 21 |
| abstract_inverted_index.state-of-the-art | 167 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.56289656 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |