How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866929238292037632 |
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| author | Lemberger, Pirmin Saillenfest, Antoine |
| author_facet | Lemberger, Pirmin Saillenfest, Antoine |
| contents | We introduce a new dataset named WikiVitals which contains a large graph of 48k mutually referred Wikipedia articles classified into 32 categories and connected by 2.3M edges. Our aim is to rigorously evaluate the contributions of three distinct sources of information to the label prediction in a semi-supervised node classification setting, namely the content of the articles, their connections with each other and the correlations among their labels. We perform this evaluation using a Graph Markov Neural Network which provides a theoretically principled model for this task and we conduct a detailed evaluation of the contributions of each sources of information using a clear separation of model selection and model assessment. One interesting observation is that including the effect of label dependencies is more relevant for sparse train sets than it is for dense train sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01235 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents Lemberger, Pirmin Saillenfest, Antoine Machine Learning Statistics Theory 62-08 G.3 We introduce a new dataset named WikiVitals which contains a large graph of 48k mutually referred Wikipedia articles classified into 32 categories and connected by 2.3M edges. Our aim is to rigorously evaluate the contributions of three distinct sources of information to the label prediction in a semi-supervised node classification setting, namely the content of the articles, their connections with each other and the correlations among their labels. We perform this evaluation using a Graph Markov Neural Network which provides a theoretically principled model for this task and we conduct a detailed evaluation of the contributions of each sources of information using a clear separation of model selection and model assessment. One interesting observation is that including the effect of label dependencies is more relevant for sparse train sets than it is for dense train sets. |
| title | How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents |
| topic | Machine Learning Statistics Theory 62-08 G.3 |
| url | https://arxiv.org/abs/2304.01235 |