Hierarchical Link Clustering in Complex Networks

Identifying modular network structure is generally a problem of finding the correct community membership of each node in a network. An alternative approach, clustering links, naturally accounts for real world characteristics such as strong community overlap, multi-partite structure, and hierarchical organization. By introducing a pair-wise link similarity, we use a hierarchical clustering method to identify relevant communities in real-world examples such as biological networks. Our results reveal previously hidden organization of communities.