Iterative embedding and reweighting of complex networks reveals community structure
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929438808080384 |
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| author | Kovács, Bianka Kojaku, Sadamori Palla, Gergely Fortunato, Santo |
| author_facet | Kovács, Bianka Kojaku, Sadamori Palla, Gergely Fortunato, Santo |
| contents | Graph embeddings learn the structure of networks and represent it in low-dimensional vector spaces. Community structure is one of the features that are recognized and reproduced by embeddings. We show that an iterative procedure, in which a graph is repeatedly embedded and its links are reweighted based on the geometric proximity between the nodes, reinforces intra-community links and weakens inter-community links, making the clusters of the initial network more visible and more easily detectable. The geometric separation between the communities can become so strong that even a very simple parsing of the links may recover the communities as isolated components with surprisingly high precision. Furthermore, when used as a pre-processing step, our embedding and reweighting procedure can improve the performance of traditional community detection algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10813 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Iterative embedding and reweighting of complex networks reveals community structure Kovács, Bianka Kojaku, Sadamori Palla, Gergely Fortunato, Santo Physics and Society Graph embeddings learn the structure of networks and represent it in low-dimensional vector spaces. Community structure is one of the features that are recognized and reproduced by embeddings. We show that an iterative procedure, in which a graph is repeatedly embedded and its links are reweighted based on the geometric proximity between the nodes, reinforces intra-community links and weakens inter-community links, making the clusters of the initial network more visible and more easily detectable. The geometric separation between the communities can become so strong that even a very simple parsing of the links may recover the communities as isolated components with surprisingly high precision. Furthermore, when used as a pre-processing step, our embedding and reweighting procedure can improve the performance of traditional community detection algorithms. |
| title | Iterative embedding and reweighting of complex networks reveals community structure |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2402.10813 |