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Influenced node discovery in a temporal contact network based on common nodes.

Jinjing Huang1, Xi Wang1,2

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Summary
This summary is machine-generated.

Discovering influenced nodes in temporal networks is challenging. This study introduces common nodes on diffusion paths, enabling the identification of more influenced nodes with fewer verifications in temporal contact networks.

Keywords:
infection probabilityinfluenced nodesinformation diffusiontemporal contact networktemporal diffusion path

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Area of Science:

  • Computer Science
  • Network Science
  • Data Mining

Background:

  • Information diffusion in temporal networks is uncertain.
  • Previous methods identify only N influenced nodes for N verifications.
  • Limited verifications restrict the discovery of influenced nodes.

Purpose of the Study:

  • To develop a method for discovering more influenced nodes with limited verifications.
  • To leverage common nodes on temporal diffusion paths for efficient influence identification.
  • To enhance the accuracy and efficiency of influence maximization in temporal networks.

Main Methods:

  • Proposed the concept of common nodes on temporal diffusion paths.
  • Developed three algorithms based on the common nodes idea.
  • Applied algorithms to search influenced nodes in temporal contact networks.

Main Results:

  • Demonstrated that common nodes can be identified as influenced without direct verification.
  • Showed the possibility of finding more than N influenced nodes for N verifications.
  • Experimental results confirm the effectiveness of the proposed algorithms in finding more influenced nodes.

Conclusions:

  • The common nodes approach significantly improves the discovery of influenced nodes in temporal networks.
  • This method offers a more efficient way to identify influence spread under limited verification constraints.
  • The proposed algorithms provide a practical solution for influence maximization problems in dynamic network environments.