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• W4205746784 abstract "In this work, we focus on improving high-order proximity of unsupervised homogeneous network embedding (NE). NE is proposed to represents every vertex of networks into a low-dimensional vector and meanwhile preserves network information as most as possible. Adjacency matrices retain most of the information in network data, and directly characterize the first-order proximity. In consideration of structure information in networks, we devote ourselves to mining valuable information in adjacency matrices at a deeper level. Under the objective, many NE methods proposed to calculate high-order proximity by the powers of adjacency matrices, which is not well- designed enough as they may contain redundancy information. To further explore high-order proximity, we propose to redefine high-order proximity in a more intuitive way. Specifically, we calculate the strength of high-order proximity by shortest paths between nodes and weights along with the paths, which is closely related to the edge weights. Consequently, for unweighted networks, we evaluate edge weights based on endpoint degrees. In comprehensive experiments on real-world network datasets, our method outperforms other competitive methods in downstream machine learning tasks such as network reconstruction and node classification." @default.
• W4205746784 created "2022-01-25" @default.
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• W4205746784 date "2021-09-01" @default.
• W4205746784 modified "2023-09-26" @default.
• W4205746784 title "EPINE: Enhanced Proximity Information Network Embedding" @default.
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• W4205746784 doi "https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00032" @default.
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