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Dynamic social network-oriented influence maximization analysis method

A technology of social networking and analysis methods, applied in the direction of instruments, data processing applications, forecasting, etc., can solve problems such as loss of validity, and achieve the effect of reasonable running time

Pending Publication Date: 2018-09-07
SHANDONG UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, many distributions in real social networks conform to the power-law distribution, including time factors. For example, concert tickets will be sold quickly before the concert, but after the concert, the news loses its validity. sex

Method used

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  • Dynamic social network-oriented influence maximization analysis method
  • Dynamic social network-oriented influence maximization analysis method
  • Dynamic social network-oriented influence maximization analysis method

Examples

Experimental program
Comparison scheme
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Embodiment

[0072] 1. Dataset and Experimental Setup

[0073] In this example, five publicly available datasets from SNAP, Oregon dataset, CA_HepPh dataset, Email dataset, Slashdot dataset and Web_Stanford dataset, are used. The Oregon dataset is an autonomous system containing 9 graphs, which is an undirected graph. The CA_HepPh data set comes from a high-energy physics theory collaborator network and is an undirected graph. The Email dataset comes from the Enron email communication network, which covers all electronic communications in about 500,000 emails and is an undirected graph. The Slashdot dataset comes from a technology-related news website. The link relationship between users is formed through the following relationship between friends, so it is a directed graph. The Web_Stanford dataset comes from the Stanford University website. Nodes in the network represent websites, and hyperlinks between websites form directed links, so it is also a directed graph. The static structura...

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Abstract

The invention discloses a dynamic social network-oriented influence maximization analysis method. The method specifically comprises the steps of (1) obtaining an activation probability, and adding a time factor into the activation probability by using power law distribution of an influence delay distribution function; (2) building an influence propagation model LAIC; (3) executing a greedy algorithm, and calculating an initial marginal income of each node by utilizing the greedy algorithm; and (4) optimizing an original greedy algorithm by using a CELF algorithm, and improving the efficiency of searching for seed nodes through sub-mode characteristics of the influence function and an influence priority queue. By analyzing the effect of the time factor in influence propagation, the power law distribution of the distribution function, consistent with real social network node degree distribution is used, and finally an excellent result and reasonable running time are achieved in selectingTOP-K nodes with highest influence, so that the problem of dynamic social network influence maximization is effectively solved.

Description

technical field [0001] The invention relates to the field of social networks, and proposes a dynamic social network-oriented influence maximization analysis method GLAIC (Greedy based on LAIC model). Background technique [0002] In recent years, with the rise of social networks, more and more users like to share their thoughts and opinions on social platforms, making social networks play an increasingly important role in information dissemination. Therefore, understanding how information disseminates and diffuses in social networks has become one of the core issues of current researchers. Suppose a company wants to promote their new product. At this time, the company will select some influential users, hoping that they can recommend this product to their friends and friends of friends through word of mouth. And this way of dissemination of social network influence has been widely used in many fields, such as viral marketing and recommendation systems. [0003] A key probl...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/00
CPCG06Q10/04G06Q10/063G06Q50/01
Inventor 仇丽青于金凤贾玮
Owner SHANDONG UNIV OF SCI & TECH
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