t-PINE: Tensor-based Predictable and Interpretable Node Embeddings
This work addresses limitations in graph representation learning for researchers and practitioners, offering a novel approach that enhances interpretability and performance, though it is incremental in building upon existing multi-view and decomposition techniques.
The paper tackles the problem of improving efficacy, visualization, and interpretability in graph representation learning by proposing t-PINE, a method that uses multi-view information and CP decomposition to learn explicit and implicit node representations, resulting in performance gains of up to 158.6% in Micro-F1 for multi-label classification tasks.
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.