Graph data processing method and device, computer equipment and storage medium

A processing method and graph data technology, applied in the field of big data, can solve problems such as poor performance of ultra-large-scale network mining, multi-iteration time and computing resources, and long computing time

Active Publication Date: 2021-01-12
TENCENT TECH (SHENZHEN) CO LTD
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  • Claims
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Problems solved by technology

However, this recursive pruning method digs up according to the core value of 1, 2, ..., k, which consumes more iterat

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  • Graph data processing method and device, computer equipment and storage medium
  • Graph data processing method and device, computer equipment and storage medium
  • Graph data processing method and device, computer equipment and storage medium

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Embodiment Construction

[0092] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0093] The graph data processing method provided by this application can be applied to such as figure 1 shown in the application environment. Wherein, the terminal 102 communicates with the server 104 through the network. The terminal 102 interacts with the terminal 102 through the server, and the server 104 can obtain the interaction data formed when the terminal 102 interacts on the network, and generate a network diagram according to the interaction data. In one embodiment, the server 104 obtains the degree of association of each node in the network graph; splits a de...

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Abstract

The invention relates to a graph data processing method and device, computer equipment and a storage medium. The method relates to a data mining technology, and comprises the following steps: obtaining the association degree of each node in a network graph; splitting a dense sub-graph from the network graph according to the association degree of each node; based on the dense subgraph, determiningstable nodes in the network graph and the kernel number of the stable nodes; obtaining a sparse sub-graph in the network graph according to the nodes except the stable nodes in the network graph and the connecting edges between the nodes; and determining the kernel number of each node in the sparse subgraph based on the sparse subgraph and the stable nodes. By the adoption of the method, subgraphmining of a super-large-scale network can be supported, memory occupation can be greatly reduced in the mining process, it is avoided that much iteration time and computing resources are wasted on non-important nodes, and mining performance is improved.

Description

technical field [0001] The present application relates to the technical field of big data, in particular to a graph data processing method, device, computer equipment and storage medium. Background technique [0002] With the continuous improvement and popularization of Internet technology, more and more users join various network platforms, thus forming a large-scale network. It is of great research value to analyze and mine the hidden information in these large-scale networks. . [0003] The K-Core algorithm is a subgraph mining algorithm that can mine closely related subgraphs from a complex network. For example, it can mine abnormal behavior groups among buyers or sellers from a transaction network, and can also find out the entire transaction A buyer or seller at the heart of the network. [0004] The current K-core mining algorithm mainly adopts the recursive pruning method, that is, iteratively determines the number of cores of each node in each network starting fro...

Claims

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Application Information

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IPC IPC(8): G06F16/22G06F16/28
CPCG06F16/2228G06F16/284G06F16/9024G06N20/00G06Q50/01G06Q20/389G06Q20/4016
Inventor 许杰李晓森欧阳文肖品陶阳宇
Owner TENCENT TECH (SHENZHEN) CO LTD
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