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Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks.

Koduru Hajarathaiah1, Murali Krishna Enduri1, Satish Anamalamudi1

  • 1Department of Computer Science and Engineering, SRM University-AP, Amaravati 522502, India.

Entropy (Basel, Switzerland)
|May 28, 2022
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Summary
This summary is machine-generated.

Identifying influential nodes in complex networks is key for information spreading. Our new Nearest Neighborhood Trust PageRank (NTPR) method efficiently finds these key nodes using structural attributes and neighbor trust.

Keywords:
PageRankcentrality measurecomplex networksinfluential nodessimilarity ratiotrust value

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Identifying influential nodes is crucial for understanding information diffusion in complex networks.
  • Existing centrality measures (e.g., PageRank, betweenness) often have high time complexity, limiting their use in large-scale networks.
  • There is a need for centrality measures that leverage both network structure and node attributes.

Purpose of the Study:

  • To propose a novel centrality measure, Nearest Neighborhood Trust PageRank (NTPR), for identifying influential nodes.
  • To develop a method that considers structural attributes of neighbors and trust values.
  • To evaluate the effectiveness of NTPR in real-world networks.

Main Methods:

  • Developed the Nearest Neighborhood Trust PageRank (NTPR) measure.
  • NTPR incorporates degree ratio, node similarity, and trust values of neighbors and nearest neighbors.
  • Evaluated NTPR on various real-world networks using SIR and independent cascade models.

Main Results:

  • The proposed NTPR method effectively identifies influential nodes in complex networks.
  • NTPR demonstrated superior performance in maximizing information influence compared to existing basic centrality measures.
  • The method's reliance on local and attribute-based information addresses scalability issues.

Conclusions:

  • NTPR offers an efficient and effective approach for computing influential nodes in large-scale networks.
  • The method's ability to integrate structural and trust-based attributes enhances its applicability in diverse network analysis tasks.
  • This research contributes a valuable tool for applications like viral marketing and rumor control.