BLANK
发表于 2025-3-27 00:12:58
Kinematic-Based Force-Directed Graph Embedding,ain the acceleration of each node. The method is intuitive, parallelizable, and highly scalable. We evaluate our method on several graph analysis tasks and show that it achieves competitive performance compared to state-of-the-art unsupervised embedding techniques.
FRONT
发表于 2025-3-27 03:04:39
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MAIM
发表于 2025-3-27 07:57:38
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大洪水
发表于 2025-3-27 10:36:23
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预定
发表于 2025-3-27 14:28:33
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皮萨
发表于 2025-3-27 19:45:06
https://doi.org/10.1007/978-3-031-57515-0Conference Proceedings; Graph Theory; Complex Systems; Computer Science; Data Science; Social Networks; Ne
chalice
发表于 2025-3-27 22:38:17
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高贵领导
发表于 2025-3-28 02:49:28
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inspired
发表于 2025-3-28 08:16:51
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混乱生活
发表于 2025-3-28 11:38:09
https://doi.org/10.1007/978-3-662-03377-7ous) MMD model is an innovation diffusion model, similar to the Bass model, which includes four decision variables (the 4Ps of Marketing: Product, Price, Place, Promotion). We introduce the Inhomogenous MMD (IMMD) model and we conduct two separate experiments: one based on simulation and another one