  Efficient and robust online processing technique of irregularly structured
data is crucial in the current era of data abundance. In this paper, we propose
a graph/network version of the classical adaptive Sign algorithm for online
graph signal estimation under impulsive noise. Recently introduced graph
adaptive least mean squares algorithm is unstable under non-Gaussian impulsive
noise and has high computational complexity. The Graph-Sign algorithm proposed
in this work is based on the minimum dispersion criterion and therefore
impulsive noise does not hinder its estimation quality. Unlike the recently
proposed graph adaptive least mean p-th power algorithm, our Graph-Sign
algorithm can operate without prior knowledge of the noise distribution. The
proposed Graph-Sign algorithm has a faster run time because of its low
computational complexity compared to the existing adaptive graph signal
processing algorithms. Experimenting on steady-state and time-varying graph
signals estimation utilizing spectral properties of bandlimitedness and
sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph
signal estimation performance under impulsive noise modeled by alpha stable,
Cauchy, Student's t, or Laplace distributions.
