T-PNE: Tensor-Based Predictable Node Embeddings

Abstract

Graph representations have increasingly grown in popularity during the last years. Existing embedding approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification), there is still room for improvement in different aspects, like effectiveness. In this paper, we propose, t-PNE, a method that addresses this limitation. Contrary to baseline methods, which generally learn explicit node representations by solely using an adjacency matrix, t-PNE avails a multi-view information graph---the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view---in order to learn explicit and implicit node representations, using the Canonical Polyadic (a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful and highly predictable representations. Extensive experiments show that t-PNE drastically outperforms baseline methods by up to 158.6% with respect to Micro-F1, in several multi-label classification problems.