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Social network influence maximization method based on community structure

A technology of social network and influence, applied in the field of social network, it can solve the problems of sparse community connection, not considering the network structure, etc., so as to improve the accuracy and operation efficiency, and solve the problem of maximizing the influence of social network.

Active Publication Date: 2018-09-04
SHANDONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of the current work does not take into account the actual structure of the network. Each network has the characteristics of a community structure, that is, the community is closely connected and the connections between communities are sparse.

Method used

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  • Social network influence maximization method based on community structure
  • Social network influence maximization method based on community structure
  • Social network influence maximization method based on community structure

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0088] 1. Data set and experimental setup

[0089] In this example, four publicly available datasets from SNAP, HepTh dataset, Brightkite dataset, Epinions dataset and Amazon dataset of different scales, are used. The HepTh dataset comes from a network of high-energy physics theory collaborators and is an undirected graph. The Brightkite dataset is a location-based social network that is an undirected graph. The Epinions dataset comes from the trust network, which is a link relationship formed by members of the Epinions website choosing partial trust to comment, so it is a directed graph. The Amazon dataset comes from the Amazon purchase website. If two products in the website are frequently purchased together, there will be a link relationship, so there is also a directed graph. The statistics of static structural features of these four datasets are shown in Table 1.

[0090] Table 1: Statistics of static structure characteristics of experimental data

[0091]

[0092]...

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Abstract

The invention discloses a social network influence maximization method based on a community structure. The method comprises the following specific processes that: (1) dividing a community, forming a candidate node set, dividing a network to identify a kernel node and a boundary node in the network, and forming the candidate node set; (2) heuristically selecting nodes, and for each node in the candidate node set, verifying potential influence through a node degree, a community scale, a connected community number and influence weight so as to heuristically select and add the node with the highest influence into a seed set; and (3) executing a greedy algorithm, and utilizing the greedy algorithm to select and add the node with the highest marginal income into the seed set. By use of the method, the function of the community structure in influence spreading is analyzed to further improve accuracy and operation efficiency for mining initial seed nodes, and the problem of social network influence maximization is effectively solved.

Description

technical field [0001] The invention relates to the field of social networks, in particular to a method for maximizing the influence of social networks based on community structure. Background technique [0002] In recent years, with the rise of social networks, more and more social platforms such as Facebook, Twitter and Google+ have attracted widespread attention. As the carrier of social network, these platforms enable various information to be disseminated on social network. How to maximize the dissemination of this information through these social platforms and allow more users to accept this information is called the "influence maximization problem". The problem of maximizing the influence of social networks is a hot issue in social network research, and has great application value in the fields of marketing, disease transmission and rumor control. [0003] The problem of maximizing social network influence is how to select Top-K seed nodes for propagation, so as to ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 仇丽青于金凤范鑫
Owner SHANDONG UNIV OF SCI & TECH
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