Transcription of Structural Deep Network Embedding - SIGKDD
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Structural deep Network Embedding Daixin Wang1 , Peng Cui1 , Wenwu Zhu1. Tsinghua National Laboratory for Information Science and Technology 1. Department of Computer Science and Technology, Tsinghua University. Beijing, China ABSTRACT ment targeting often needs to cluster the users into communities in Network Embedding is an important method to learn low-dimensional the social Network . Therefore, mining the information in the net- representations of vertexes in networks, aiming to capture and pre- work is very important. One of the fundamental problems is how serve the Network structure. Almost all the existing Network em- to learn useful Network representations [5].
In order to address the structure-preserving and sparsity prob-lems in the deep model, we further propose to exploit the first-order and second-order proximity [26] jointly into the learning process. The first-order proximity is the local pairwise similarity only be-tween the vertexes linked by edges, which characterizes the local network ...
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