The goal of EAN is extracting the same features from the topological structure and attributed information according to our extractive network, while ...
Nov 23, 2019 · We propose a deep learning model named Extractive Convolutional Adversarial Network (ECAN) for network embedding.
Abstract—Network embedding has attracted more and more researchers recently. Although many algorithms focus on topo- logical information, there exists a ...
-EAN [5] : This algorithm aims to extract the latent space from labels, the attributed information, and the topological structure, based on the generative ...
The goal of EAN is extracting the same features from the topological structure and attributed information according to our extractive network, while ...
This work proposes a deep learning model named Extractive Convolutional Adversarial Network (ECAN) for network embedding, which aims to extract the latent ...
Extractive convolutional adversarial networks for network embedding. Xiaorui Qin, Yanghui Rao, Haoran Xie, Jian Yin, Fu Lee Wang. Anthology ID: DBLP:journals ...
In this paper, we develop a model named Topical Adversarial Capsule Network (TACN) for textual network embedding, which extracts a low-dimensional latent space ...
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Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classi- fication, link prediction and network ...