Synergistic Graph Fusion via Encoder Embedding
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910501295882240 |
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| author | Shen, Cencheng Priebe, Carey E. Larson, Jonathan Trinh, Ha |
| author_facet | Shen, Cencheng Priebe, Carey E. Larson, Jonathan Trinh, Ha |
| contents | In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect: for sufficiently large vertex size, the accuracy of vertex classification consistently benefits from the incorporation of additional graphs. We establish the mathematical foundation for the method, including the asymptotic convergence of the embedding, a sufficient condition for asymptotic optimal classification, and the proof of the synergistic effect for vertex classification. Our comprehensive simulations and real data experiments provide compelling evidence supporting the effectiveness of our proposed method, showcasing the pronounced synergistic effect for multiple graphs from disparate sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_18051 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Synergistic Graph Fusion via Encoder Embedding Shen, Cencheng Priebe, Carey E. Larson, Jonathan Trinh, Ha Social and Information Networks Machine Learning In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect: for sufficiently large vertex size, the accuracy of vertex classification consistently benefits from the incorporation of additional graphs. We establish the mathematical foundation for the method, including the asymptotic convergence of the embedding, a sufficient condition for asymptotic optimal classification, and the proof of the synergistic effect for vertex classification. Our comprehensive simulations and real data experiments provide compelling evidence supporting the effectiveness of our proposed method, showcasing the pronounced synergistic effect for multiple graphs from disparate sources. |
| title | Synergistic Graph Fusion via Encoder Embedding |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2303.18051 |