Robust Graph Topology Learning and Application in Stock Market Inference

Ramezani-Mayiami, Mahmoud; Skretting, Karl

doi:10.1109/icsipa45851.2019.8977789

Cited by 1 publication

(1 citation statement)

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“…They showed the efficiency of their proposed method on the multi-variate timeseries data collected from a real IIoT based four stage compressor manufacturing plant. In [48], the effect of distortion on the trading data has been discussed, where the stock market's impact on this data is characterized by a graph [49]. There are also other studies in the GSP framework considering the noise effect on the observations, e.g.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Topology Learning and noise removal from network data

Mayiami

Hajimirsadeghi

Skretting

et al. 2021

Discov Internet Things

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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.

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Section: Introductionmentioning

confidence: 99%