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- W3118486513 abstract "The network is a crucial form of representing the relationship between entities. Recently, with the rapid development of machine learning, the application of graph embedding in complex network systems such as social networks and information networks has become more and more widespread. From early Euclidean distance of the network structure to the shallow word embedding model, to the present deep auto-encoder that combines with additional information of nodes, graph embedding technology is undergoing a great leap. Its core idea is to compress network information into low-dimensional dense vectors, and then use the obtained feature vectors for practical network analysis tasks. The survey introduces the current development status of graph embedding technology and the upcoming challenges brought to us by the limitations of processing data. We take the time sequence as a rough baseline to systematically classify and summarize the traditional and current mainstream graph embedding methods based on different fundamental principles. In addition, we also enumerate the applications of the algorithm in various scenarios such as link prediction, node classification. As the frontier topic and the cutting-edge technology in the field of network representation learning, graph embedding possesses extremely vital academic value and application prospects." @default.
- W3118486513 created "2021-01-18" @default.
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- W3118486513 date "2020-12-04" @default.
- W3118486513 modified "2023-09-27" @default.
- W3118486513 title "A Survey of Algorithms and Applications Related with Graph Embedding" @default.
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- W3118486513 doi "https://doi.org/10.1145/3444370.3444568" @default.
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