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Social network influence maximization method based on local and global influences

A social network and influence technology, applied in the field of social network, can solve problems such as low efficiency, achieve the effect of improving accuracy, solving the problem of maximizing social network influence, and designing reasonable

Pending Publication Date: 2020-06-12
SHANDONG UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this algorithm pursues the accuracy of results too much, which makes it very inefficient
All in all, all four algorithms are overly focused on either efficiency or accuracy without making a proper compromise between the two

Method used

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  • Social network influence maximization method based on local and global influences
  • Social network influence maximization method based on local and global influences
  • Social network influence maximization method based on local and global influences

Examples

Experimental program
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Effect test

Embodiment

[0058] 1. Dataset and Experimental Setup

[0059] In this example, six public datasets of different sizes from SNAP (http: / / snap.stanford.edu / data) are used: CA-GrQc dataset, Wiki-Vote dataset, NetHEPT dataset, CA-CondMat datasets, p2p-Gnutella31 dataset and soc-Epinions1 dataset. The NetHEPT data set is a co-authored relationship network of papers in the "High Energy Physics-Theory" part from 1991 to 2003; the Wiki-Vote data set is the "referendum" network in Wikipedia; the CA-GrQc data set is extracted from "Generalized Relativity and Quantum Cosmology" part of the collaborative network; CA-CondMat dataset is a collaborative network of Arxiv condensed matter matter; p2p-Gnutella31 dataset is a peer-to-peer network of Gnutella since August 31, 2002; soc-Epinions1 dataset It's Epinions.com's Who Trusts Who Network. The static structural feature statistics of these four datasets are shown in Table 1.

[0060] Table 1: Statistics of static structural features of experimental ...

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Abstract

The invention discloses a social network influence maximization method based on local and global influences, belongs to the field of social networks, and provides a two-stage filtering strategy of candidate vertexes, which comprises the following steps: firstly, selecting a certain number of source vertexes according to the local influence of the vertexes; then, searching ancestor vertexes of thesource vertexes, and screening a certain number of candidate vertexes according to the expected influence of the ancestor vertexes on the source vertexes; and finally, selecting the seed vertex from the candidate vertexes selected by the two-stage filtering strategy by utilizing the marginal income of the expected influence of the candidate vertexes on the source vertex. By means of the method, the problem of low time efficiency is solved, the accuracy of the influence range is improved, and the problem of social network influence maximization is effectively solved.

Description

technical field [0001] The invention belongs to the field of social networks, and in particular relates to a method for maximizing social network influence based on local and global influences. Background technique [0002] With the development and popularization of Internet technology, more and more people establish contact with others through social networks. As an important communication medium, social network plays an increasingly important role in public opinion control, information dissemination and public services. [0003] In marketing, budgets or resources are often limited. In this case, how to maximize the scope of information dissemination is a very important content. In order to solve this problem, we need to select a small number of influential people under the constraints of budget or resources to maximize the final spread of information, which is to maximize influence. However, the problem turns out to be an NP-hard problem. Since the problem of influence ...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06F30/20
CPCG06Q50/01
Inventor 仇丽青田相波
Owner SHANDONG UNIV OF SCI & TECH
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