Attributed network embedding with dual fusion strategies

Abstract

ABSTRACT Attributed network embedding (ANE) maps nodes in a network into a low-dimensional space while preserving the intrinsic essence of node attribute and network topology. Inc orporating node attribute and network topology with more deeply and more harmoniously is a critical and challenging issue in the ANE, because node attribute and network topology are two kinds of heterogeneous information. Existing approaches fuse two kinds of heterogeneous information at different stages: i.e. before, during or after the learning process. In fact, fusions at different stages have their own advantages and disadvantages. To maximise the profit of utilising the attributed and networked information in ANE, we propose an Attributed Network Embedding model with Dual Fusion strategies (abbr. ANEDF), which consists of both mutually beneficial components: early fusion component for capturing the latent complementarity and late fusion component for extracting the unique and distinctive information from node attribute and network topology. The two components are co-trained during the learning process, which promotes information interaction and captures the consensus of heterogeneous information. Extensive experiments with the tasks of node classification, node clustering, link prediction and visualisation on eight publicly available networks have been conducted to evaluate the effectiveness and rationality of the proposed model. The experimental results demonstrate that ANEDF obtains the best classification, clustering and link prediction performance on 6–7 of 8 datasets, respectively.