Abstract
Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(|E|) in sparse networks, where |E| is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.
Highlights
The science of networks is a fundamental discipline across biology, social sciences, computer science and other fields
One of possible reasons of existing works comes from that the time complexity is at least quadratic, which always takes several hours to deal with large bipartite networks
U second order neighbors N N (u) are the same types as v neighbors N (v). Due to this point of view, we propose Local Jaccard Distance to deal with community detection in bipartite networks
Summary
The science of networks is a fundamental discipline across biology, social sciences, computer science and other fields. One of possible reasons of existing works comes from that the time complexity is at least quadratic, which always takes several hours to deal with large bipartite networks They have the problem of resolution limit, which leads to inaccurate detection of small communities [23]. A novel method using distance dynamics has been proposed to detect two-mode communities in large bipartite networks It is inspired by interactions in human society such that there are more interactions within the same community but less between different ones. It has time complexity O(|E|) in sparse networks and obtains accurate partition of communities as well Experiments demonstrate that it is faster than other methods in real sparse networks with thousands of vertices and edges.
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