Efficient Detection of Communities in Biological Bipartite Networks.

Methods to efficiently uncover community structures are required in a number of biological applications where observing tightly-knit groups of vertices ("communities") can offer insights into the structural and functional building blocks. Classical applications of community detection have largely focused on unipartite networks. However, due to increased availability of biological data from various sources, there is now a need for handling heterogeneous networks which are built out of multiple types of objects. In this paper, we address the problem of identifying communities from biological bipartite networks---i.e., networks where interactions are observed between two different types of objects. Toward detecting communities in such bipartite networks, we make the following contributions: i) (metric) we propose a variant of bipartite modularity; ii) (algorithms) we present an efficient algorithm called biLouvain that implements a set of heuristics toward fast and precise community detection in bipartite networks (https://github.com/paolapesantez/biLouvain); and iii) (experiments) we present a thorough experimental evaluation of our algorithm including comparison to other state-of-the-art methods to identify communities in bipartite networks. Experimental results show that biLouvain identifies communities that have a comparable or better quality (as measured by bipartite modularity) than existing methods, while significantly reducing the time-to-solution between one and four orders of magnitude.