Abstract
As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.
Highlights
Popular research in the field of network science is to mine hidden information under the network structure
Community detection is an important aspect of complex network research, and we can see the presence of the community in various fields, such as detecting the intensive group organization in a social network [1], the different muscle tissue composed by various genes found in the gene protein networks [2], and so on
(1) The hierarchical clustering method defines the similarity or distance between network nodes by the topology of the given network, groups network nodes into a tree hierarchy by single-connection or full-connection hierarchical clustering, and cross-cuts the tree diagram according to actual needs to obtain the community structure
Summary
Popular research in the field of network science is to mine hidden information under the network structure. In 2017, Tamron et al proposed the NILPA algorithm [13], where the node importance is judged according to the degree of the node, and the node similarity matrix is formed according to the random walk theory; these two points are combined to form new measure criteria to update the label These algorithms improved the stability and accuracy, but at the cost of increasing the time complexity. (1) The first involves merging the multilayer network into a single-layer network, and carrying out community detection using the existed community detection algorithm [14,15,16], but this method may ignore the topological information in each layer of a multilayer network [17]. Considering the weight of the network as one of the methods to evaluate the centrality of the nodes, the MSH-LPA algorithm is proposed
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