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Y V Nandini1, T Jaya Lakshmi1,2, Murali Krishna Enduri1

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This study introduces novel average centrality measures for link prediction in complex networks. The new methods significantly improve accuracy in predicting future connections compared to existing techniques.

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centrality measurescomplex networkslink prediction measures

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Link prediction is vital for understanding network evolution across diverse fields.
  • Existing methods often rely on local or global centrality measures for similarity scoring.
  • There is a need for improved link prediction accuracy.

Purpose of the Study:

  • To propose novel link prediction methods using average centrality measures.
  • To evaluate the effectiveness of these new measures against existing techniques.
  • To enhance the accuracy of identifying future network connections.

Main Methods:

  • Developed four novel similarity measures: Similarity based on Average Degree (SACD), Betweenness (SACB), Closeness (SACC), and Clustering Coefficient (SACCC).
  • Calculated node centrality scores, averaged them across the graph, and derived similarity scores using common neighbors.
  • Applied centrality scores to common neighbors, identifying nodes with above-average centrality.

Main Results:

  • The proposed average centrality measures significantly outperformed existing local similarity measures.
  • Achieved an average improvement of 24% in Area Under the Receiver Operating Characteristic (AUROC) and 49% in Area Under Precision-Recall (AUPR) over existing methods.
  • Demonstrated a 31% AUROC and 51% AUPR improvement over recent link prediction measures.

Conclusions:

  • The novel approach using average centrality measures offers superior performance for link prediction.
  • These findings have implications for improving network analysis in various domains.
  • The proposed methods provide a more accurate way to predict future links in complex networks.