1 research outputs found
Quantitative Function and Algorithm for Community Detection in Bipartite Networks
Community detection in complex networks is a topic of high interest in many
fields. Bipartite networks are a special type of complex networks in which
nodes are decomposed into two disjoint sets, and only nodes between the two
sets can be connected. Bipartite networks represent diverse interaction
patterns in many real-world systems, such as predator-prey networks,
plant-pollinator networks, and drug-target networks. While community detection
in unipartite networks has been extensively studied in the past decade,
identification of modules or communities in bipartite networks is still in its
early stage. Several quantitative functions proposed for evaluating the quality
of bipartite network divisions are based on null models and have distinct
resolution limits. In this paper, we propose a new quantitative function for
community detection in bipartite networks, and demonstrate that this
quantitative function is superior to the widely used Barber's bipartite
modularity and other functions. Based on the new quantitative function, the
bipartite network community detection problem is formulated into an integer
programming model. Bipartite networks can be partitioned into reasonable
overlapping communities by maximizing the quantitative function. We further
develop a heuristic and adapted label propagation algorithm (BiLPA) to optimize
the quantitative function in large-scale bipartite networks. BiLPA does not
require any prior knowledge about the number of communities in the networks. We
apply BiLPA to both artificial networks and real-world networks and demonstrate
that this method can successfully identify the community structures of
bipartite networks.Comment: 18 pages, 5 figure