Monitoring network data by considering the correlation of network feature statistics

Panpan Zhou, Dennis Lin, Xiaoyue Niu, Zhen He

Research output: Contribution to journalConference article

Abstract

Network monitoring has wide applications in computer and social network surveillance, pathological diagnosis in neuroscience and bioscience among others. Motivated by a real example of brain networks, we focus our interests on monitoring networks by considering correlations of feature statistics. Structural statistics of numbers of edges, stars and triangles, are adopted to summarize the main features of a network-density, degree variability, and transitivity. A multivariate chart is proposed to monitor the multiple statistics simultaneously, which has not been paid much attention to in previous studies. A simulation study is conducted to compare the performances of the multivariate chart and individual charts for the structural statistics as well as a model-based approach as a benchmark. The results show that the multivariate chart for the structural statistics perform well in most scenarios. In particular, it is more advantageous in timely detecting large shifts of connection propensity and degree variability locally and globally. A real case of monitoring Enron email networks is analyzed as an illustration.

Original languageEnglish (US)
JournalProceedings of International Conference on Computers and Industrial Engineering, CIE
Volume2018-December
StatePublished - Jan 1 2018
Event48th International Conference on Computers and Industrial Engineering, CIE 2018 - Auckland, New Zealand
Duration: Dec 2 2018Dec 5 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'Monitoring network data by considering the correlation of network feature statistics'. Together they form a unique fingerprint.

  • Cite this