Link prediction in real-world multiplex networks via layer reconstruction method

Abstract

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

“…In a multiplex network, the task of link prediction is to estimate the likelihood of potential links between nodes that are currently unconnected in the target layer, utilizing the information from both the target layer itself and the other auxiliary layers. Previous research [40] showed that similarity in interlayer structural features can enhance link pre-diction performance in multiplex networks using the layer reconstruction method and experimental analysis on real-world multiplex networks from various domains. Tang et al [5] introduced a semi-supervised learning method that considers interlayer structural information to predict links in the target layer of a multiplex network.…”

Reconstructing Sparse Multiplex Networks with Application to Covert Networks

“…These approaches give similarity scores for missing or unobserved links between any pair of two nodes, then links with high similarity scores will be predicted to exist. For example, in [13], the eigenvectors of the layer adjacency matrix are used to measure the layer topological similarity and then the topological similarity (element of the layer similarity matrix) of the unconnected nodes is used to predict if a link exists between them. Berlusconi et al [14] and Calderoni et al [15] apply multiple similarity metrics, such as common neighbor and resource allocation, to identify missing links in a criminal network.…”

“…They demonstrated that these similarity metrics can identify possible missing links in criminal networks with noise or incomplete information. However, the performance of similarity-based methods depends on the structure of specific networks [16]; therefore, prior knowledge about the networks under study is usually required to improve the predictive performance [13]. This approach is infeasible for the network reconstruction problem in this study because the nodes and links to be inferred are completely missing without any connection with observed nodes.…”

Modeling the Public Transport Networks: A Study of Their Efficiency

“…ey are Apache Giraph and Apache Graphx. Reference [14] demonstrates that similarities with respect to structural features (eigenvectors) optimize the link prediction task in multiplex networks. is is done using a layer reconstruction method (LRM), which considers the unconnected node pairs in the target layer as similar, provided that they are not only analogous from the point of view of the target layer but also from the perspective of other layers.…”