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• W3190166355 abstract "Graphs can be used to describe complex systems. Recently, whole graph embedding (graph representation learning) can compress a graph into a compact lower-dimension vector while preserving intrinsic properties, earning much attention. However, most graph embedding methods have problems such as tedious parameter tuning or poor explanation. This paper presents a simple and hyperparameter-free whole graph embedding method based on the DHC (Degree, H-index, and Coreness) theorem and Shannon Entropy (E), abbreviated as DHC-E. The DHC-E can provide a trade-off between simplicity and quality for supervised classification learning tasks involving molecular, social, and brain networks. Moreover, it performs well in lower-dimensional graph visualization. Overall, the DHC-E is simple, hyperparameter-free, and explainable for whole graph embedding with promising potential for exploring graph classification and lower-dimensional graph visualization." @default.
• W3190166355 created "2021-08-16" @default.
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• W3190166355 date "2021-08-04" @default.
• W3190166355 modified "2023-10-16" @default.
• W3190166355 title "Hyperparameter-free and Explainable Whole Graph Embedding" @default.
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• W3190166355 doi "https://doi.org/10.48550/arxiv.2108.02113" @default.
• W3190166355 hasPublicationYear "2021" @default.
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