Abstract: An estimator is provided that can be used to get an estimate of final graph size and peak memory usage of the graph during loading, based on sampling of the graph data and using machine learning (ML) techniques. A data sampler samples the data from files or databases and estimates some statistics about the final graph. The sampler also samples some information about property data. Given the sampled statistics gathered and estimated by the data sampler, a graph size estimator estimates how much memory is required by the graph processing engine to load the graph. The final graph size represents how much memory will be used to keep the final graph structures in memory once loading is completed. The peak memory usage represents the memory usage upper bound that is reached by the graph processing engine during loading.

Abstract: Techniques for automatically generating tests using a large language model (LLM) are provided. In one technique, a set of positive training samples for training a first LLM is stored. Based on that set, a set of correction training samples is generated, each sample including an error from processing a faulty test of particular code. A second LLM is trained based on those samples. A first test, of code, that was generated by the first LLM is received. A first result of processing the first test is generated. In response to determining that the first result indicates an error in processing the first test, a first correction prompt is generated based on the first result. The first correction prompt is input into the second LLM that outputs a second test that is a corrected version of the first test. A second result of processing the second test is generated.

Abstract: A sampling procedure is performed for paths on a multi-hop distributed graph that includes vertices partitioned on a plurality of machines; a sampled path includes first and second vertices hosted on first and second machines respectively. The sampling procedure includes communicating, by the first machine to the second machine, first path information comprising an identifier for the second vertex and an identifier for a target vertex of the sampled path; the target vertex is hosted on a target host machine. The procedure further includes communicating edge information by the first machine to the target host machine, and communicating feature information by the second machine to the target host machine. Communication of the edge information and communication of the feature information are deferred relative to communication of the first path information.

Abstract: A sampling procedure is performed for paths on a multi-hop distributed graph that includes vertices partitioned on a plurality of machines; a sampled path includes first and second vertices hosted on first and second machines respectively. The sampling procedure includes communicating, by the first machine to the second machine, first path information comprising an identifier for the second vertex and an identifier for a target vertex of the sampled path; the target vertex is hosted on a target host machine. The procedure further includes communicating edge information by the first machine to the target host machine, and communicating feature information by the second machine to the target host machine. Communication of the edge information and communication of the feature information are deferred relative to communication of the first path information.

Abstract: An estimator is provided that can be used to get an estimate of final graph size and peak memory usage of the graph during loading, based on sampling of the graph data and using machine learning (ML) techniques. A data sampler samples the data from files or databases and estimates some statistics about the final graph. The sampler also samples some information about property data. Given the sampled statistics gathered and estimated by the data sampler, a graph size estimator estimates how much memory is required by the graph processing engine to load the graph. The final graph size represents how much memory will be used to keep the final graph structures in memory once loading is completed. The peak memory usage represents the memory usage upper bound that is reached by the graph processing engine during loading.

Abstract: In an embodiment, a computer hosts and operates an input neural layer of an artificial neural network that generates, based on all of the features of a first vertex of a first vertex type in a graph, an embedding of the first vertex. The embedding of the first vertex has a predefined size that does not depend on the first vertex type. The input neural layer generates, based on all of the features of a first edge of a first edge type in the graph, an embedding of the first edge. A subsequent neural layer of the artificial neural network generates an embedding of a second vertex of a second vertex type in the graph, and this generating is based on: the embedding of the first vertex and all of the features of the second vertex, including a particular feature that is not a feature of the first vertex type.