One way to increase or decrease contrast is to raise your s to a given power, e.g.
s**0.5 to decrease contrast or
s**2 to increase contrast. It is worthwhile noting that this can be done by mcl itself, using the
-pi (pre-inflation) option, e.g.
-pi 0.5 or
-pi 2, so there is no need to create many different input files. In fact, mcl has a more general transformation option called
-tf. The same effect can be achieved by supplying e.g.
-tf 'pow(0.5)' or
-tf 'pow(2)' (quotes are required to prevent the shell from interpreting these parentheses).
Now, to judge the merits of different conversions, you will need to wade in and have a look at the data and the clusterings you get and preferably use your knowledge of the domain to make informed choices. If you have gold-standard data (manual curation of some sort) it is possible to be a bit more systematic, for example in the case where the gold standard annotation can be viewed as a clustering.
Choose an edge-weight (similarity) cutoff such that the topology of the network becomes informative; i.e. too many edges or too few edges yield little discriminative information in the absence/presence structure of edges. Choose it such that no edges connect things you consider very dissimilar, and that edges connect things you consider somewhat similar to quite similar. In the case of mcl, the dynamic range in edge weight between 'a bit similar' and 'very similar' should be, as a rule of a thumb, one order of magnitude, i.e. two-fold or five-fold or ten-fold, as opposed to varying from 0.9 to 1.0.