Bayesian Topology Learning and noise removal from network data

Mahmoud Ramezani Mayiami, Mohammad Hajimirsadeghi, Karl Skretting, Xiaowen Dong, Rick S. Blum, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.

Original languageEnglish (US)
Article number11
JournalDiscover Internet of Things
Issue number1
StatePublished - Dec 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Hardware and Architecture
  • Human-Computer Interaction
  • Information Systems
  • Electrical and Electronic Engineering


  • Bayesian inference
  • Graph signal processing
  • Internet of things
  • Signal representation
  • Topology learning


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