Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach

Abstract

In real world, a heterogeneous information network (HIN) is often dynamic due to the time varying features of the nodes, and uncertain due to missing values and noise. In this paper, we investigate the problem of reducing the uncertainty of a dynamic HIN, which is an important task for HIN analysis. The challenges are three-fold, the heterogeneity of features, the heterogeneity of constraints, and the dynamic uncertainty. We propose a novel approach, called fusing reconstruction (FRec), which reconstructs the uncertain snapshots of a dynamic HIN in a homogeneous feature space combining two fusions, the fusion of heterogeneous features and the fusion of heterogeneous constraints. To address the challenge of the heterogeneity of features, we propose an invertible fusing transformation (IFT) as the first part of FRec. IFT is a bidirectional transformation, which is able to learn unified latent homogeneous feature representations for heterogeneous nodes and transform them back to the raw heterogeneous feature space by its invertibility. To address the challenge of the heterogeneity of constraints and the challenge of dynamic uncertainty, we propose a heterogeneous constraints fusion based tensor reconstruction model (HCF-TRM) as the second part of FRec. HCF-TRM is able to denoise the uncertain snapshots of a dynamic HIN and recovers the missing values by fusing the spatial smoothness constraint and the temporal smoothness constraint into the tensor reconstruction. At last, the extensive experiments conducted on real datasets and synthetic datasets verify the effectiveness and scalability of FRec.