Graph data processing method and device and storage medium

Abstract

The invention discloses a graph data processing method and device and a storage medium, and can be applied to the field of maps. The method comprises the following steps: segmenting initial graph data into first partitions; determining a partition sub-graph corresponding to the first partition; performing graph segmentation on each partition sub-graph to obtain a sub-graph segmentation partition corresponding to each partition sub-graph; dividing the sub-graph into partitions as graph nodes to generate thumbnails; and segmenting the thumbnail into a second partition, and partitioning nodes in the initial graph data based on the second partition to obtain a target node set. In this way, the parallel graph data processing process is achieved, due to the fact that the multiple partitions are adopted, different devices can be adopted for processing, in other words, each device carries out graph segmentation on the data of the device, mapping reduction can be carried out through thumbnails, the advantages of parallel processing are fully utilized, and the graph data processing efficiency is improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular, to a method, device and storage medium for processing graph data. Background technique [0002] With the rapid development of Internet technology, people have higher and higher requirements for social networks. A social network can be constructed through graph data. The dots of the graph data represent entities, and the edges represent the relationship between two entities. By processing the graph data, the social network can be associated or simplified. [0003] Generally, the METIS algorithm can be used to process the graph data, that is, by continuously compressing the graph structure, two nodes will be merged into a new node. Therefore, based on the new nodes, a smaller graph is obtained. [0004] However, in the scenario of large-scale data processing, the METIS algorithm can put all the data in the memory, which is relatively high on the computer memory, and...

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Problems solved by technology

[0004] However, in the scenario of large-scale data processing, the METIS algorithm can put all the data in memory, which has a high impact on computer memory, and has high computing costs and long running time, which affects the efficiency of graph data processing.

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