Graph data processing method and device, computing equipment, storage medium and program product

Abstract

The invention provides a graph data processing method. The graph data processing method comprises the steps of obtaining directed graph data; creating an associated cardinal counter for each node on a parameter server based on the acquired graph data; initializing an associated cardinality counter based on the node information of each node; and iteratively updating the radix counters until the expected number of iterations is reached, in each iteration, for each source node, obtaining the radix counter associated with the source node from the parameter server, transmitting the radix counter to all tail nodes associated with the source node, and for each tail node, transmitting the radix counter to the parameter server. Updating a cardinality counter associated with the tail node on a parameter server based on the cardinality counter; and determining the number of nodes in the neighborhood of the expected order of at least one node in the plurality of nodes based on the iteratively updated cardinality counter. The method can be used for multiple applications which can be abstracted into graph data for processing, including the fields of finance, social contact, vehicle traffic and the like.

Description

technical field [0001] The present application relates to the field of computer technology, and specifically relates to a graph data processing method, a graph data processing device, a computing device, a computer-readable storage medium, and a computer program product. Background technique [0002] With the continuous popularization and development of computer networks, people are increasingly participating in various network relationships, such as social networks, financial transaction networks, and Internet of Vehicles. Typically, graphs can be used to describe these network relationships. Specifically, the nodes in the graph can correspond to the various participants in the network relationship, and the edges can correspond to the network associations that exist between different participants. For example, in a social network, the edges can correspond to the connections between different participants. Friend relationship, communication relationship, etc., or, in a fina...

AI Technical Summary

Problems solved by technology

However, in most current network relationships, there are often a huge number of participants, and there may be intricate network associations between different participants. Therefore, the graphs used to describe these network relationships will also contain massive amounts of data

In a general processor or computing engine, performing the above statistics based on such a graph containing massive data will occupy a large data storage space and computing resources, and cause high data interaction pressure inside the processor or computing engine

Images