  Inference of new biological knowledge, e.g., prediction of protein function,
from protein-protein interaction (PPI) networks has received attention in the
post-genomic era. A popular strategy has been to cluster the network into
functionally coherent groups of proteins and predict protein function from the
clusters. Traditionally, network research has focused on clustering of nodes.
However, why favor nodes over edges, when clustering of edges may be preferred?
For example, nodes belong to multiple functional groups, but clustering of
nodes typically cannot capture the group overlap, while clustering of edges
can. Clustering of adjacent edges that share many neighbors was proposed
recently, outperforming different node clustering methods. However, since some
biological processes can have characteristic "signatures" throughout the
network, not just locally, it may be of interest to consider edges that are not
necessarily adjacent. Hence, we design a sensitive measure of the "topological
similarity" of edges that can deal with edges that are not necessarily
adjacent. We cluster edges that are similar according to our measure in
different baker's yeast PPI networks, outperforming existing node and edge
clustering approaches.
