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Variation Autoencoder Based Network Representation Learning for Classification

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Figure 1: Architecture of our model. wi can be seen as a word of the content information, ui is a nodein the network, ui is a representation vector learned by the Content2Vec Module, xi is a vector of theadjacency matrix
Table 1: Macro-F1 score on Citeseer-M10 Network
Figure 2: Performance of each strategy on different training proportion p

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