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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Solving the non-submodular network collapse problems via Decision Transformer.

Kaili Ma1, Han Yang1, Shanchao Yang2

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, New Territories, 999077, Hong Kong, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 30, 2024
PubMed
Summary
This summary is machine-generated.

We introduce DT-NC, a novel framework for the Network Collapse Problem (NCP). DT-NC effectively models sequential actions, outperforming existing methods on complex graph analysis tasks.

Keywords:
Collapsed k-coreDecision TransformerGraph neural networkNetwork collapseNetwork dismantling

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Area of Science:

  • Graph theory
  • Network analysis
  • Machine learning

Background:

  • Network Collapse Problem (NCP) is crucial for graph analytics but NP-hard.
  • Traditional greedy and existing learning methods struggle with combinatorial effects and sequential decision-making in NCPs, especially for non-submodular functions.

Purpose of the Study:

  • To propose a unified framework, DT-NC, that adapts the Decision Transformer for Network Collapse Problems.
  • To address the limitations of existing methods in capturing sequential actions and combinatorial effects in NCPs.

Main Methods:

  • DT-NC framework adapts the Decision Transformer architecture.
  • The model considers historical actions to capture the combinatorial effect of selected vertices.
  • Evaluated on diverse NCPs and graph sizes.

Main Results:

  • DT-NC effectively models dependencies among selected vertices, handling non-submodular measure functions.
  • Demonstrated superior performance compared to state-of-the-art methods across various NCPs.
  • DT-NC shows excellent transferability and generalizability.

Conclusions:

  • DT-NC offers a powerful new approach for solving Network Collapse Problems.
  • The framework's ability to model sequential decision-making and combinatorial effects enhances performance, particularly for non-submodular NCPs.