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- W2898290394 abstract "Networks such as social networks, airplane networks, and citation networks are ubiquitous. To apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experiments demonstrate the effectiveness of the proposed framework." @default.
- W2898290394 created "2018-11-02" @default.
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- W2898290394 date "2018-08-01" @default.
- W2898290394 modified "2023-10-14" @default.
- W2898290394 title "Local and Global Information Preserved Network Embedding" @default.
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- W2898290394 doi "https://doi.org/10.1109/asonam.2018.8508496" @default.
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