Today I read a paper titled “Evolutionary method for finding communities in bipartite networks”
The abstract is:
An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks
We show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization
To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection
The high efficiency of the MAGA is based on the following three improvements we make
First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate
This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them
Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase
Third, we present a modified mutation rule which by incorporating related operation can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process
Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks