US 12,216,629 B2
Data processing method and apparatus, computerreadable medium, and electronic device
Xiaosen Li, Shenzhen (CN); Jie Xu, Shenzhen (CN); Wen Ouyang, Shenzhen (CN); Yangyu Tao, Shenzhen (CN); and Pin Xiao, Shenzhen (CN)
Assigned to TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED, Shenzhen (CN)
Filed by TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED, Shenzhen (CN)
Filed on Oct. 12, 2022, as Appl. No. 17/964,778.
Application 17/964,778 is a continuation of application No. PCT/CN2021/132221, filed on Nov. 23, 2021.
Claims priority of application No. 202011626906.6 (CN), filed on Dec. 31, 2020.
Prior Publication US 2023/0033019 A1, Feb. 2, 2023
Int. Cl. G06F 16/22 (2019.01); G06F 16/901 (2019.01); G06Q 50/00 (2012.01)
CPC G06F 16/22 (2019.01) [G06F 16/9024 (2019.01); G06Q 50/01 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer implemented data processing method, the method comprising:
acquiring a relationship graph network, the relationship graph network comprising nodes for representing interacting objects and edges for representing interactive relationships between multiple interacting objects;
partitioning the relationship graph network to obtain a first number of partition graph networks, wherein a partition graph network includes part of the nodes and edges in the relationship graph network;
performing, based on the first number of partition graph networks, core degree mining on the relationship graph network in a distributed computing mode through a device cluster comprising multiple computing devices, and iteratively updating node core degrees of all or some of the nodes in the relationship graph network, each computing device in the device cluster being configured to perform computing tasks of the core degree mining on the relationship graph network;
pruning the relationship graph network according to the node core degrees to remove some of the nodes and edges in the relationship graph network; and
compressing the device cluster to remove some of the computing devices in the device cluster, when a network scale of the relationship graph network corresponding to the first number of the partitioned graph networks meets a network compression condition, comprising:
repartitioning, according to the network scale, the relationship graph network to obtain a second number of partition graph networks, the second number being smaller than the first number;
invoking one or more computing devices in the multiple computing devices based on the second number of partition graph networks; and
in response to the network scale being reduced to satisfy a specified condition, transforming from the distributed computing mode to a standalone computing mode by: selecting a single computing device from the device cluster as a target device for standalone computing of the relationship graph network, and removing the other computing devices except the target device from the device cluster, wherein the target device has a computing capability to perform the computing tasks of the core degree mining on the relationship graph network with the reduced network scale.