A method and device for discovering important users based on broadcast network

Abstract

The present invention belongs to the technical field of social communication analysis, and specifically relates to a method and device for discovering important users based on a communication network, wherein the method includes: step 1, constructing an information communication network through clicking and sharing behaviors for activities requiring analysis, using The attenuation iterative algorithm calculates the spread influence of each node in a single communication network; step 2, adjusts the spread influence according to the number of first-level interaction nodes of each node; step 3, according to each node in multiple The role in the dissemination network, calculate the absolute influence of each node, and sort; step 4, perform data normalization processing, convert the absolute influence into relative influence, and sort, to get important users. The invention avoids the possibility of the Pagerank algorithm falling into the terminal node, effectively improves the calculation speed of the algorithm; avoids the possibility of excessive iteration; comprehensively considers the functions of nodes in multiple networks, and improves the accuracy of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of social communication analysis, and in particular relates to a method and device for discovering important users based on a communication network. Background technique [0002] With the continuous development of social networks, active information sharing and dissemination among people has become more and more common. Communicators publish the information they want to promote on the Internet through social media, and the information is seen by other individuals in their relationship network, which can cause multi-level secondary dissemination, and the exposure rate of information increases sharply. In the entire communication network, compared with other nodes, the key influence nodes have a great effect on the effect and scope of communication. Therefore, it is particularly important to locate the key influence nodes in the communication network in practical applications. [0003] When selecting key infl...

AI Technical Summary

Problems solved by technology

[0003] When selecting key influential nodes in the prior art, the algorithms often used include: 1. Degree centrality, that is, the more direct neighbor nodes of a node, the greater its influence. The disadvantage is that only the local information of the node in propagation is considered

2. The calculation of Pagerank is to regard the dissemination network as a directed graph. Each dissemination is recorded as a vote from the disseminator to the disseminated. Through iterative and recursive methods, the influence score of each node in the network is finally obtained. , the disadvantage is that in the propagation network, the connection between nodes is relatively sparse and scattered, the calculation efficiency of this algorithm is not high, and it is easy to fall into the possibility of terminal nodes

Images