Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
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_ | 1866907985466359808 |
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| author | Shen, Cencheng Park, Youngser Priebe, Carey E. |
| author_facet | Shen, Cencheng Park, Youngser Priebe, Carey E. |
| contents | In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a normalized one-hot graph encoder and a rank-based cluster size measure. Through extensive simulations, we demonstrate the excellent numerical performance of our proposed graph encoder ensemble algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_11290 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection Shen, Cencheng Park, Youngser Priebe, Carey E. Social and Information Networks Machine Learning In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a normalized one-hot graph encoder and a rank-based cluster size measure. Through extensive simulations, we demonstrate the excellent numerical performance of our proposed graph encoder ensemble algorithm. |
| title | Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2301.11290 |