The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspondence of power flow to message-passing in GNNs, their performance on power grids is not well-understood. We argue that there is a gap between GNN research driven by benchmarks which contain graphs that differ from power grids in several important aspects. Additionally, inductive learning of GNNs across multiple power grid topologies has not been explored with real-world data.

Abstract Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires multi-modal reasoning from both computer vision and natural language processing. We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, and natural language processing techniques. Concretely, our method is based on performing context-driven, sequential reasoning based on the scene entities and their semantic and spatial relationships. As a first step, we derive a scene graph that describes the objects in the image, as well as their attributes and their mutual relationships. Subsequently, a reinforcement learning agent is trained to autonomously navigate in a multi-hop manner over the extracted scene graph to generate reasoning paths, which are the basis for deriving answers. We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs. Our results show that we keep up with human performance on manually curated scene graphs. Moreover, we find that Graphhopper outperforms another state-of-the-art scene graph reasoning model on both manually curated and automatically generated scene graphs by a significant margin.

Most latent feature methods for recommender systems learn to encode user preferences and item characteristics based on past user-item interactions. While such approaches work well for standalone items (e.g., books, movies), they are not as well suited for dealing with composite systems. For example, in the context of industrial purchasing systems for engineering solutions, items can no longer be considered standalone. Thus, latent representation needs to encode the functionality and technical features of the engineering solutions that result from combining the individual components. To capture these dependencies, expressive and context-aware recommender systems are required. In this paper, we propose NECTR , a novel recommender system based on two components: a tensor factorization model and an autoencoder-like neural network. In the tensor factorization component, context information of the items is structured in a multi-relational knowledge base encoded as a tensor and latent representations of items are extracted via tensor factorization. Simultaneously, an autoencoder-like component captures the non-linear interactions among configured items. We couple both components such that our model can be trained end-to-end. To demonstrate the real-world applicability of NECTR , we conduct extensive experiments on an industrial dataset concerned with automation solutions. Based on the results, we find that NECTR outperforms state-of-the-art methods by approximately 50% with respect to a set of standard performance metrics.