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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Signed network representation with novel node proximity evaluation.

Pinghua Xu1, Wenbin Hu2, Jia Wu3

  • 1School of Computer Science, National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence, Wuhan University, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces three novel node proximity metrics based on signed average first-passage time (SAFT) for signed network representation. These metrics improve performance in sign and link prediction tasks.

Keywords:
Network representationNode proximitySigned social network

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Area of Science:

  • Graph theory
  • Network science
  • Machine learning

Background:

  • Signed network representation is crucial for fields like recommendation systems.
  • Existing methods often struggle to effectively capture complex network structures and edge signs.
  • Node proximity evaluation is a key challenge in network representation learning.

Purpose of the Study:

  • To propose novel node proximity metrics for signed networks.
  • To introduce a new distance metric, signed average first-passage time (SAFT).
  • To enhance the performance of network representation in downstream tasks.

Main Methods:

  • Developed three new node proximity metrics derived from SAFT.
  • SAFT is based on random-walk quantities and captures high-order network structure and edge signs.
  • Applied proposed metrics to network representation and evaluated on sign and link prediction tasks.

Main Results:

  • The proposed proximity metrics demonstrated advantages in network representation.
  • Empirical results show improved performance in sign prediction tasks.
  • Empirical results show improved performance in link prediction tasks.

Conclusions:

  • The novel SAFT-based proximity metrics offer a significant advancement for signed network representation.
  • The proposed approach effectively captures network structure and edge signs.
  • The method shows practical utility in recommendation platforms and similar applications.