  The enormous amount of data to be represented using large graphs exceeds in
some cases the resources of a conventional computer. Edges in particular can
take up a considerable amount of memory as compared to the number of nodes.
However, rigorous edge storage might not always be essential to be able to draw
the needed conclusions. A similar problem takes records with many variables and
attempts to extract the most discernible features. It is said that the
``dimension'' of this data is reduced. Following an approach with the same
objective in mind, we can map a graph representation to a $k$-dimensional space
and answer queries of neighboring nodes mainly by measuring Euclidean
distances. The accuracy of our answers would decrease but would be compensated
for by fuzzy logic which gives an idea about the likelihood of error. This
method allows for reasonable representation in memory while maintaining a fair
amount of useful information, and allows for concise embedding in
$k$-dimensional Euclidean space as well as solving some problems without having
to decompress the graph. Of particular interest is the case where $k=2$.
Promising highly accurate experimental results are obtained and reported.
