  We present a nonparametric graphical model. Our model uses an undirected
graph that represents conditional independence for general random variables
defined by the conditional dependence coefficient (Azadkia and Chatterjee
(2021)). The set of edges of the graph are defined as
  $E=\{(i,j):R_{i,j}\neq 0\}$, where $R_{i,j}$ is the conditional dependence
coefficient for $X_i$ and $X_j$ given $(X_1,\ldots,X_p) \backslash
\{X_{i},X_{j}\}$.
  We propose a graph structure learning by two steps selection procedure:
first, we compute the matrix of sample version of the conditional dependence
coefficient $\widehat{R_{i,j}}$; next, for some prespecificated threshold
$\lambda>0$ we choose an edge $\{i,j\}$ if
  $ \left|\widehat{R_{i,j}} \right| \geq \lambda.$ The graph recovery structure
has been evaluated on artificial and real datasets. We also applied a slight
modification of our graph recovery procedure for learning partial correlation
graphs for the elliptical distribution.
