08/12/2020

Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization

Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li

Keywords:

Abstract Paper Similar Papers

Abstract: Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6308-exploiting-node-content-for-multiview-graph-convolutional-network-and-adversarial-regularization
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at COLING 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 11:16 
 0
 0
 0
 0
 5:15 
 0
 0
 0
 0
 3:15 
 0
 0
 0
 0
 17:33 
 0
 1
 0
 2
 3:13 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 19:54 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 4:53 
 0
 0
 0
 0
 6:00 
 0
 0
 0
 0
 3:38 
 0
 0
 0
 0
 14:49 
 0
 0
 0
 0
 13:33 
 0
 0
 0
 1
 5:15 
 0
 0
 0
 0
 5:07 
 0
 0
 0
 0
 5:13 
 0
 0
 0
 0
 15:01 
 0
 0
 0
 0
 8:20 
 0
 0
 0
 0
 9:50 
 0
 0
 0
 0
 10:50 
 0
 0
 0
 0
 19:51 
 0
 0
 0
 0
 19:10 
 0
 0
 0
 0
 10:04 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 14:46 
 0
 0
 0
 0
 18:16 
 0
 0
 0
 0
 5:15 
 0
 0
 0
 0
 4:39 
 0
 0
 0
 0
 14:31 
 0
 0
 0
 0
 11:47 
 0
 0
 0
 0
 14:14 
 0
 0
 0
 0
 2:43 
 0
 0
 0
 0
 9:51 
 0
 0
 0
 0
 11:54 
 0
 0
 0
 0
 15:26 
 0
 0
 0
 0
 15:28 
 0
 0
 0
 0
 11:06 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 19:55 
 0
 0
 0
 0
 16:32