Fast Geometric Embedding for Node Influence Maximization
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913066403233792 |
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| author | Kolpakov, Alexander Rivin, Igor |
| author_facet | Kolpakov, Alexander Rivin, Igor |
| contents | Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we introduce an efficient force layout algorithm that embeds a graph into a low-dimensional space, where the radial distance from the origin serves as a proxy for various centrality measures. We evaluate our method on multiple graph families and demonstrate strong correlations with degree, PageRank, and paths-based centralities. As an application, it turns out that the proposed embedding allows one to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast Geometric Embedding for Node Influence Maximization Kolpakov, Alexander Rivin, Igor Social and Information Networks Artificial Intelligence Machine Learning E.1; G.2.2; G.4 Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we introduce an efficient force layout algorithm that embeds a graph into a low-dimensional space, where the radial distance from the origin serves as a proxy for various centrality measures. We evaluate our method on multiple graph families and demonstrate strong correlations with degree, PageRank, and paths-based centralities. As an application, it turns out that the proposed embedding allows one to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm. |
| title | Fast Geometric Embedding for Node Influence Maximization |
| topic | Social and Information Networks Artificial Intelligence Machine Learning E.1; G.2.2; G.4 |
| url | https://arxiv.org/abs/2506.07435 |