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\section{Related Work}  Signal processing on graphs as defined in  \cite{Shuman} is an emergent field that extends high-dimensional data analysis to networks and other irregular domains. The graph spectral frequency domain is processed by fundamental operations such as filtering, translation, modulation, dilation and downsampling. A graph signal is defined by a finite collection of samples at each graph vertex. To compute information at each graph node a small neighbourhood of the vertex is considered. The graph Laplacian and its eigenvectors and eigenvalues encodes the graph connectivity. The graph Laplacian eigenvector corresponding to lower eigenvalues are smoother. Other graphs matrices are the normalized graph Laplacian and the random walk matrix. More research has to be done to know when to use each matrix. The authors define generalized operators for signals in graphs. Extensions include: analyzing directed graphs and vertexes with a time serie associated.  The authors of  \cite{Miller} propose the use of detection and estimation theory as defined for vector spaces with Gaussian noise in the context of graph analytics framework creating a new research area at the intersection of this domains. It is applied in the situational awareness cyber security to detect suspicious activity. Small subsets of vertices whose interactions do not fit the typical behaviour are identified. Relationships modeled as a graph are dificult to be analized in the Detection Theory framework. Translation and scaling are difficult to define for combinatorial and discrete graphs.  \cite{Sizov_2010}