Abstract: A data processing method is provided. In the method, a historical walk vertex adjacent to a target walk vertex is determined. An edge transition probability between the target walk vertex and each of a set of next possible vertexes in a first out-neighbor set is determined according to first out-edge information. A to-be-reached vertex of the set of next possible vertexes in the first out-neighbor set is determined according to the edge transition probabilities. Second out-edge information corresponding to the target walk vertex is generated based on the first out-neighbor set. Walking from the target walk vertex to the to-be-reached vertex is performed. The second out-edge information is transmitted to the to-be-reached vertex. Further, a random walk sequence corresponding to the target walk vertex is generated based on a walk quantity corresponding to the target walk vertex reaching a preset threshold for walk steps.

Abstract: A data processing method is provided. In the method, a historical walk vertex adjacent to a target walk vertex is determined. An edge transition probability between the target walk vertex and each of a set of next possible vertexes in a first out-neighbor set is determined according to first out-edge information. A to-be-reached vertex of the set of next possible vertexes in the first out-neighbor set is determined according to the edge transition probabilities. Second out-edge information corresponding to the target walk vertex is generated based on the first out-neighbor set. Walking from the target walk vertex to the to-be-reached vertex is performed. The second out-edge information is transmitted to the to-be-reached vertex. Further, a random walk sequence corresponding to the target walk vertex is generated based on a walk quantity corresponding to the target walk vertex reaching a preset threshold for walk steps.

Abstract: Embodiments of this disclosure include a data processing method. In the method, a training sample set that includes a plurality of graph computing task training samples is obtained. At least one performance indicator feature of each of the graph computing task training samples is extracted. The at least one performance indicator feature includes one or more of a graph data feature, a graph processing platform feature, a graph algorithm feature, and a machine hardware feature. A target performance prediction model is generated based on a mapping relationship between actual execution times of the graph computing task training samples and the performance indicator features. According to at least one performance indicator feature of an inputted graph computing task test sample, a predicted execution time of the graph computing task test sample is output based on the target performance prediction model.