Method and device for obtaining multi-hop neighbor node

Abstract

The invention discloses a method and a device for obtaining a multi-hop neighbor node. The method and the device are used for improving the calculation efficiency of inquiring the multi-hop neighbor node in a big graph. The method for obtaining the multi-hop neighbor node comprises the steps of obtaining a zero-hop neighbor of each node in a topological network to obtain a full node zero-hop neighbor set; when a first node serves as an appointed node, calculating a k+1-hop neighbor set of the first node through an iterative calculation process below, wherein the iterative calculation process comprises a step a of obtaining the k-hop neighbor set of the first node and a step b of connecting the k-hop target node in the k-hop neighbor set of the first node and two node pairs with the same initial nodes in the full node zero-hop neighbor set into a node pair, and obtaining the k+1-hop neighbor set of the first node after the connection of the node pair is finished; and when the hop number k+1 of the k+1-hop neighbor set of the first node is equal to kmax, outputting the k+1-hop neighbor set of the first node and a corresponding k+1 hop path.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for acquiring multi-hop neighbor nodes. Background technique [0002] A social network is a network of connected individuals and organizations. Now there are many social networks, such as Facebook, Twitter, paper citation network, Skype communication network, blog network and Renren, etc., showing a prosperous scene. In a social network, a vertex represents an object. For example, a paper, a sensor, a person, etc. can be used as a vertex. Edges represent the relationship between vertices. The relationship can include cooperative papers, friends, teachers, etc. Student relationship, citation relationship of papers, etc. [0003] Multi-hop neighbors have a wide range of applications on large graphs, such as community discovery, advertisement recommendation or friend recommendation, link prediction, finding friends of friends, and so on. On a large graph, a ...

AI Technical Summary

Problems solved by technology

Therefore, the existing BFS method for calculating multi-hop neighbors will generate a large amount of network communication costs, especially for some dense graphs or power-law graphs. A large amount of information transmission will cause network congestion, which will reduce the cost of querying multi-hop neighbors in large graphs. Node Computational Efficiency

Images