  Studying the topology of so-called {\em real networks}, that is networks
obtained from sociological or biological data for instance, has become a major
field of interest in the last decade. One way to deal with it is to consider
that networks are built from small functional units called {\em motifs}, which
can be found by looking for small subgraphs whose numbers of occurrences in the
whole network of interest are surprisingly high. In this paper, we propose to
define motifs through a local over-representation in the network and develop a
statistic which allows us to detect them limiting the number of false positives
and without time-consuming simulations. We apply it to the Yeast gene
interaction data and show that the known biologically relevant motifs are found
again and that our method gives some more information than the existing ones.
