Abstract: A method of training a machine learning model includes receiving training data comprising a graph structure and one or more feature attributes and determining an encoded graph based on applying the machine learning model to the graph structure and the one or more feature attributes. The machine learning model comprises a graph convolutional network layer. The encoded graph comprises one or more nodes and one or more paths connecting the one or more nodes. The method also includes selecting a plurality of positive samples through random walks along the one or more paths of the encoded graph, selecting a plurality of negative samples from the encoded graph by randomly sampling the one or more nodes of the encoded graph, determining a loss value, and updating, based on the loss value, one or more learnable parameter values of the machine learning model.

Abstract: The present disclosure relates to generating a search graph or search index to aid in receiving a search query and identifying results of a dataset based on the search query. For example, systems disclosed herein may generate a navigable search graph including vertices representative of objects or points within a dataset that enables a computing device having access to the search graph to navigate vertices of the graph along an identified path until arriving at a point within the search graph that corresponds to a value associated with the search query. Upon identifying a location within the graph corresponding to the search query, systems disclosed herein may identify a neighborhood of points (e.g., vertices) corresponding to items from the dataset and output a set of results for the search query representative of determined results for the search query.

Abstract: The present disclosure relates to generating a search graph or search index to aid in receiving a search query and identifying results of a dataset based on the search query. For example, systems disclosed herein may generate a navigable search graph including vertices representative of objects or points within a dataset that enables a computing device having access to the search graph to navigate vertices of the graph along an identified path until arriving at a point within the search graph that corresponds to a value associated with the search query. Upon identifying a location within the graph corresponding to the search query, systems disclosed herein may identify a neighborhood of points (e.g., vertices) corresponding to items from the dataset and output a set of results for the search query representative of determined results for the search query.

Abstract: The present disclosure relates to generating a search graph or search index to aid in receiving a search query and identifying results of a dataset based on the search query. For example, systems disclosed herein may generate a navigable search graph including vertices representative of objects or points within a dataset that enables a computing device having access to the search graph to navigate vertices of the graph along an identified path until arriving at a point within the search graph that corresponds to a value associated with the search query. Upon identifying a location within the graph corresponding to the search query, systems disclosed herein may identify a neighborhood of points (e.g., vertices) corresponding to items from the dataset and output a set of results for the search query representative of determined results for the search query.