Case ID: M22-100P^

Published: 2022-10-10 09:16:58

Last Updated: 1697450768


Kaize Ding
Jianling Wang
Huan Liu

Technology categories

Computing & Information TechnologyPhysical Science

Technology keywords

Algorithm Development
Data Mining
Machine Learning
Neural Computing
PS-Computing and Information Technology
Social Media

Licensing Contacts

Physical Sciences Team

Systems and Algorithms for Few-Shot Node Classification on Graphs

­Node classification in real-world attributed networks is a central analytical task that is a growing research area. In real-world networks, a large portion of node classes only contain limited labeled instances.

Many prevailing graph machine learning methods typically rely upon the availability of sufficient labeled data. However, the long-tail property of real-world graphs makes those methods less effective for learning new concepts when only limited data is available. A powerful graph machine learning model should be able to quickly learn never-before-seen class labels using only a handful of labeled data. Dealing with such few-shot concepts is important and has practical applications in a number of fields.

Researchers at Arizona State University have developed a novel algorithm and system designed for graph few-shot learning for different down-stream tasks, including node classification and anomaly detection. This system is able to perform meta-learning on an attributed network and derive a highly generalizable model for handling the target classification task. This system operates by constructing a pool of semi-supervised node classification tasks to mimic the real test environment.

Related publication: Graph Prototypical Networks for Few-shot Learning on Attributed Networks

Potential Applications:

  • Social network analysis
  • Financial fraud detection
  • Drug discovery

Benefits & Advantages:

  • Can operate with limited labeled data
  • Demonstrated superior capability of few-shot node classification
  • Robust and effective model for machine learning