Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the topological dependency among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification. In this paper, we propose three methods ("Network Aggregation," "results aggregation" and "Layer Co-analysis") to project a multilayer network into a continuous vector space. On one hand, without leveraging interactions among layers, "Network Aggregation" and "results aggregation" apply the standard single-layer network embedding method to the merged graph or to each layer to find a vector space for multilayer network. On the other hand, in order to consider the influence of interactions among layers, "Layer Co-analysis" expands any single-layer network embedding method to a principled multilayer network embedding framework. By introducing the link transition probability based on information-theoretic distance between layers, this method not only uses the first and second order random walks to traverse on a layer, but also has the ability to traverse between layers by leveraging layer-wise interactions. From the evaluation, we have shown that compared with regular link prediction methods, "Layer Co-analysis" achieves the best performance on most of the datasets, while "Network Aggregation" and "results aggregation" also have better performance than regular link prediction methods.
PMNE REF proposes three methods to project a multiplex network into a continuous vector space.
Principled Multilayer Network Embedding
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Computer Science Physics