  Graph representations have increasingly grown in popularity during the last
years. Existing representation learning approaches explicitly encode network
structure. Despite their good performance in downstream processes (e.g., node
classification, link prediction), there is still room for improvement in
different aspects, like efficacy, visualization, and interpretability. In this
paper, we propose, t-PINE, a method that addresses these limitations. Contrary
to baseline methods, which generally learn explicit graph representations by
solely using an adjacency matrix, t-PINE 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, highly predictable, and gracefully interpretable
representations. Having good interpretable representations provides a good
guidance to understand how each view contributes to the representation learning
process. In addition, it helps us to exclude unrelated dimensions. Extensive
experiments show that t-PINE drastically outperforms baseline methods by up to
158.6% with respect to Micro-F1, in several multi-label classification
problems, while it has high visualization and interpretability utility.
