Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures

Koduru Hajarathaiah, Murali Krishna Enduri, Satish Anamalamudi, Ashu Abdul, Jenhui Chen*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

3 Scopus citations

Abstract

The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node's functional importance and structural attributes. To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node's true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios. In the first scenario, training and testing data are sourced from the same network, highlighting the superior accuracy of the machine learning approach. In the second scenario, training data from one network and testing data from another are used, where LRACC, LRAK, and LRAD outperform the machine learning methods.

Original languageEnglish
Pages (from-to)10186-10201
Number of pages16
JournalIEEE Access
Volume12
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Complex networks
  • influential nodes
  • local centralities
  • Logistic regression
  • machine learning techniques
  • Support vector machines
  • Testing
  • Training
  • Training data
  • Urban areas

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