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.
We compare our method to three popular node clustering methods: clique percolation , greedy modularity optimization , and Infomap . Also, we compare it to the existing edge clustering algorithm, edge-SN . Briefly, is the most prominent overlapping node clustering algorithm, is the most popular modularity-based technique, and is often considered the most accurate method available . hierarchically groups adjacent edges whose non-common end-nodes share many neighbors (see below). We did not run these algorithms on the yeast networks ourselves. Instead, we use the results reported by @cite_0 who ran the algorithms on the same networks. For details on how the methods were implemented, see @cite_0 . We do explain how edge-SN was implemented, as we implement our method in the same way (except that we use a different distance metric).
@cite_0
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Network theory has become pervasive in all sectors of biology, from biochemical signalling to human societies, but identification of relevant functional communities has been impaired by many nodes belonging to several overlapping groups at once, and by hierarchical structures. These authors offer a radically different viewpoint, focusing on links rather than nodes, which allows them to demonstrate that overlapping communities and network hierarchies are two faces of the same issue.