02/02/2021

Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph

Zemin Liu, Yuan Fang, Chenghao Liu, Steven C.H. Hoi

Keywords:

Abstract Paper Similar Papers

Abstract: Node classification is an important problem on graphs. While recent advances in graph neural networks achieve promising performance, they require abundant labeled nodes for training. However, in many practical scenarios, there often exist novel classes in which only one or a few labeled nodes are available as supervision, known as few-shot node classification. Although meta-learning has been widely used in vision and language domains to address few-shot learning, its adoption on graphs has been limited. In particular, graph nodes in a few-shot task are not independent and relate to each other. To deal with this, we propose a novel model called Relative and Absolute Location Embedding (RALE) hinged on the concept of hub nodes. Specifically, RALE captures the task-level dependency by assigning each node a relative location within a task, as well as the graph-level dependency by assigning each node an absolute location on the graph to further align different tasks toward learning a transferable prior. Finally, extensive experiments on three public datasets demonstrate the state-of-the-art performance of RALE.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38948097
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 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
 14:49 
 0
 0
 0
 0
 11:16 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 2:58 
 0
 0
 0
 0
 14:12 
 0
 0
 0
 0
 15:01 
 0
 0
 0
 0
 9:30 
 0
 0
 0
 0
 9:51 
 0
 0
 0
 0
 13:27 
 0
 0
 0
 0
 7:06 
 0
 0
 0
 0
 13:35 
 0
 0
 0
 0
 13:23 
 0
 0
 0
 0
 3:38 
 0
 0
 0
 0
 12:57 
 0
 0
 0
 0
 9:39 
 0
 0
 0
 0
 19:55 
 0
 0
 0
 0
 9:36 
 0
 0
 0
 0
 14:41 
 0
 0
 0
 0
 3:15 
 0
 0
 0
 0
 15:28 
 0
 0
 0
 0
 15:02 
 0
 0
 0
 0
 12:11 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 13:53 
 0
 0
 0
 0
 13:59 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 19:59 
 0
 0
 0
 0
 15:10 
 0
 0
 0
 0
 5:57 
 0
 0
 0
 0
 3:23 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 14:56 
 0
 0
 0
 0
 13:11 
 0
 0
 0
 0
 6:07 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 14:18 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 4:56