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- W4387169932 abstract "Graph embedding can be seen as a transformation of any graph into low-dimensional vector space, where each vertex of the graph has a one-to-one correspondence with a vector in that space. The latest study in this field shows a particular interest in a slightly different approach of graph embedding, where each node is inclined to preserve a community membership to respect high-order proximity and community awareness. We investigate different options of solving both tasks jointly, so a practical solution to one problem could be shared to enhance a solution to another problem and vice-versa. We imply that many iterations of such transferring can be made to achieve better results in both problems simultaneously. As a result of our work, we introduce a model that outperforms traditional methods which consider problems of graph embedding and community detection separately." @default.
- W4387169932 created "2023-09-30" @default.
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- W4387169932 date "2023-01-01" @default.
- W4387169932 modified "2023-10-01" @default.
- W4387169932 title "Iterative Graph Embedding and Clustering" @default.
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- W4387169932 doi "https://doi.org/10.1007/978-3-031-43085-5_6" @default.
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