Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate data-bound vector images from input raster images. In some embodiments, the disclosed systems receive, from a client device, a data file and a raster image depicting at least one raster object within a scene. Additionally, the disclosed systems generate at least one scalar vector graphic object from the at least one raster object of the raster image and bind the at least one scalar vector graphic object to data from the data file. Further, the disclosed systems generate a scalar vector graphic image depicting the at least one scalar vector graphic object within the scene based on the data bound to the at least one scalar vector graphic object. The disclosed systems provide the scalar vector graphic image for display on a graphical user interface.

Abstract: In one aspect, a computer-implemented method includes accessing, by a guidance module of an analysis application executing on a processor, wildcard data associated with data in a data repository. The method further includes displaying, by the guidance module based on the wildcard data, one or more wildcard elements in a graphical user interface (GUI). The method further includes receiving, by the analysis application, selection of a first wildcard element of the one or more wildcard elements. The method further includes displaying, by the guidance module, a suggestion based on the selection of the first wildcard element.

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation.