Graph neural networks and other machine learning models offer a promising direction for machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of multimodal information to learn from. We introduce a set of new benchmark tasks for node classification on RDF-encoded knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models. For each dataset, we provide test and validation sets of at least 1000 instances, with some over 10000. Each task can be performed in a purely relational manner, or with multimodal information. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into numpy and pytorch . We compute performance for several baseline models.

Abstract We introduce a new method for finding network motifs . Subgraphs are motifs when their frequency in the data is high compared to the expected frequency under a null model . To compute this expectation, a full or approximate count of the occurrences of a motif is normally repeated on as many as 1000 random graphs sampled from the null model; a prohibitively expensive step. We use ideas from the minimum description length literature to define a new measure of motif relevance. With our method, samples from the null model are not required. Instead we compute the probability of the data under the null model and compare this to the probability under a specially designed alternative model. With this new relevance test, we can search for motifs by random sampling, rather than requiring an accurate count of all instances of a motif. This allows motif analysis to scale to networks with billions of links.