Graph kernel decomposition method based on h-hop distance

Abstract

The invention discloses a graph kernel decomposition method based on an h-hop distance, and the method comprises the steps: obtaining a to-be-decomposed big data original graph G, calculating the h-hop neighbor data of each node in the original graph G, traversing the whole original graph G to find the minimum value of the h-hop neighbors, assigning k to the minimum value, and putting all nodes with the number of the h-hop neighbors being k into a queue Q, sequentially selecting a node v from Q, deleting the node v from G and Q, when one node v is deleted, updating the number of h-hop neighbors of all nodes in the h-hop neighbors of the node v, and iteratively deleting the node with the least h-hop neighbors until all nodes are deleted. Compared with the prior art, the method does not needto repeatedly calculate the h-hop neighbors of the nodes, the calculation efficiency is higher, and the algorithm design is simple and easy to implement.

Description

technical field [0001] The present invention relates to the technical field of application scenarios related to big data mining, such as social network analysis, web network mining, etc., and particularly relates to a graph kernel decomposition method based on h-hop distance. Background technique [0002] At present, in recent years, with the development of information technology, all kinds of big data are widely used in practical applications, such as: social network, Web network, biological network and so on. Extracting hidden dense substructures from these networks is a fundamental problem in network analysis, such as mining social circles from social networks, discovering key important sites in Web networks, and identifying protein networks in biological networks. compound and so on. At present, people have proposed many models in the field of graph network mining to extract dense subgraphs in the network. Among them, the more classic model is the k-kernel model, k-kern...

AI Technical Summary

Problems solved by technology

[0011] (1) The algorithm needs to repeatedly calculate the h-hop neighbors of nodes;

[0012] (2) In order to improve computational efficiency, the optimization algorithm uses the upper and lower bounds of h-hop neighbors to reduce repeated calculations, but this algorithm is extremely dependent on the error between the upper and lower bounds of nodes and the exact value, if the upper and lower bounds If it is too far from the accurate (k,h)-Core, the performance of the algorithm will be greatly reduced

[0013] (3) Moreover, the optimization algorithm needs to divide the graph into multiple subgraphs for calculation. The size of the subgraph has a great impact on the performance of the algorithm. Therefore, the calculation efficiency of this algorithm is not very stable;

[0014] (4) Even if the optimization algorithm can improve the performance of the algorithm to a certain extent, it does not reduce the time complexity and space complexity of the algorithm in essence, so it is not general

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