Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes Article Swipe
Wei Duan
,
Junyu Xuan
,
Maoying Qiao
,
Jie Lü
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.02055
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.02055
Graph convolutional networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbour to update its representation. As a result, the representations of nodes with edges between them should be positively correlated and thus can be considered positive samples. However, there are more non-neighbour nodes in the whole graph, which provide diverse and useful information for the representation update. Two non-adjacent nodes usually have different representations, which can be seen as negative samples. Besides the node representations, the structural information of the graph is also crucial for learning. In this paper, we used quality-diversity decomposition in determinant point processes (DPP) to obtain diverse negative samples. When defining a distribution on diverse subsets of all non-neighbouring nodes, we incorporate both graph structure information and node representations. Since the DPP sampling process requires matrix eigenvalue decomposition, we propose a new shortest-path-base method to improve computational efficiency. Finally, we incorporate the obtained negative samples into the graph convolution operation. The ideas are evaluated empirically in experiments on node classification tasks. These experiments show that the newly proposed methods not only improve the overall performance of standard representation learning but also significantly alleviate over-smoothing problems.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.02055
- https://arxiv.org/pdf/2212.02055
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310827335
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https://openalex.org/W4310827335Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2212.02055Digital Object Identifier
- Title
-
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-12-05Full publication date if available
- Authors
-
Wei Duan, Junyu Xuan, Maoying Qiao, Jie LüList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.02055Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2212.02055Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2212.02055Direct OA link when available
- Concepts
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Computer science, Graph, Theoretical computer science, Convolutional neural network, Node (physics), Feature learning, Representation (politics), Artificial intelligence, Engineering, Structural engineering, Politics, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.convolutional | 1 |
| abstract_inverted_index.decomposition | 117 |
| abstract_inverted_index.non-neighbour | 66 |
| abstract_inverted_index.significantly | 210 |
| abstract_inverted_index.classification | 188 |
| abstract_inverted_index.decomposition, | 156 |
| abstract_inverted_index.over-smoothing | 212 |
| abstract_inverted_index.representation | 10, 80, 206 |
| abstract_inverted_index.message-passing | 26 |
| abstract_inverted_index.representation. | 39 |
| abstract_inverted_index.representations | 44 |
| abstract_inverted_index.non-neighbouring | 137 |
| abstract_inverted_index.representations, | 88, 99 |
| abstract_inverted_index.representations. | 147 |
| abstract_inverted_index.quality-diversity | 116 |
| abstract_inverted_index.shortest-path-base | 161 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |