deletions | additions       diff --git a/Related Work.tex b/Related Work.tex  index b98f70d..6cf4054 100644  --- a/Related Work.tex  +++ b/Related Work.tex 
...
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{Miller_2012}  \cite{Coifman_2006} is a math theoretical article in wich it is shown that in the context of manifolds, graphs, data sets and general metric spaces, diffusion processes and Markov processes can be analyzed in  a multiscale fashion very much in the spirit of classical wavelet analysis. It is proposed fast and stable algorithms for constructing orthonormal bases for the multiscale approximation spaces, and it is shown that there is an efficient scaling function and wavelet transform.