Traditional applications cope with
graph structured data by engaging
a preprocessing phase which map the
graph structured information to a
simpler representation, e.g. vectors
of reals.
However, important information, e.g.,
the topological dependency of information
on node n may be lost during the preprocessing
stage and the final result may depend,
in an unpredictable manner, on the
details of the preprocessing algorithm.
More recently, there are various approaches
[1,
2] attempting to preserve the
graph structured nature of the data
for as long as required before processing
the data. In other words, these approaches
attempt to avoid the preprocessing
step of “squashing” the graph structured
data into a vector of reals first,
and to deal with the preprocessed
data using a list based data processing
technique, rather than paying special
attention to the underlying graph
structured nature of the data. In
these recent approaches, the idea
is to encode the underlying graph
structured data using the topological
relationship among the nodes of the
graph in order to incorporate the
graph structured information in the
data processing step. Recursive neural
networks [1,
3,
4]
belong to this set of techniques and
are commonly applied in this kind
of problems. Our research group has
actively partecipated to definition
of this new computation paradigm,
contributing to the theoretical foundations
[9,
10,
11,
12]
and the experimental assessment of
the model [13,
14,
15,
16].
