Social network influence maximization method based on activeness

A social network and influence technology, which is applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems that should not be selected as seed nodes, have nothing, activate users and have low reactivation power, and achieve social network influence Effects of force maximization problem science

Inactive Publication Date: 2014-09-17
JIANGSU UNIV
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Problems solved by technology

[0004] However, it can be seen from the actual analysis that the selection effect of the initial node depends on two aspects, firstly, how to define the influence of the node, and secondly, the effect of algorithm execution also has a great influence
The independent cascading (IC) information propagation model commonly used in existing research generally calculates the influence of nodes based on the number of neighbor nodes and the activation probability of nodes to neighbor nodes. It is not difficult to analyze. There are two problems in this calculation method : The first one lies in influ

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  • Social network influence maximization method based on activeness
  • Social network influence maximization method based on activeness
  • Social network influence maximization method based on activeness

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Embodiment Construction

[0028] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0029] Such as figure 1 As shown, the implementation steps of the social network influence maximization method based on activity are as follows:

[0030] 1) Construct a simplified network G';

[0031] 2) According to the node activity ACT attribute, select the node with high activity ACT attribute value to enter the ACT node set H;

[0032] 3) Calculate the comprehensive value of node influence aps(v) according to the node activity ACT attribute value and influence size;

[0033] 4) Circularly select k seed nodes, and each time a seed node is selected, the comprehensive value aps(v) of node influence in the network must be updated.

[0034] The specific situation of the above four steps will be further introduced in detail below.

[0035] The first is the description of the AIC model. The AIC model refers to the activity-based IC model, ...

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Abstract

The invention discloses a social network influence maximization method based on activeness. The social network influence maximization method comprises the following steps: constructing a simple network G'; selecting a node with a high activeness ACT attribute value according to a node activeness ACT attribute to enter an ACT node set H; calculating a node influence synthetic value aps (v) according to the sizes of the node activeness ACT attribute value and the influence; circularly selecting k seed nodes; updating the node influence synthetic value aps (v) in a network when each seed node is selected. A result shows that the activeness attribute is introduced based on an IC model; the extension of the model enables a social network influence maximization problem to be more scientific; an ACH (Automated Clearing House) algorithm is close to an influence range of a KK greedy algorithm in an influence range and the social network influence maximization method has very good representation on timeliness.

Description

technical field [0001] The invention belongs to the application field of computer information technology, relates to a social network influence maximization technology, in particular to a method for selecting seed nodes based on activity degree to maximize social network influence. Background technique [0002] Social networks are composed of a large number of users and complex relationships among users (including family relationships, friend relationships, classmate relationships, work relationships, etc.). Different from traditional networks, the dissemination and diffusion of information in social networks depends on the relationship between users. How to make information be received by as many users as possible in the network, that is, the problem of maximizing the influence of social networks, is a current research hotspot in social networks and their applications. Richardson, M. in the literature "Mining knowledge-sharing sites for viral marketing" and Domingos, P. in ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06Q50/01
Inventor 周莲英朱锋郭远郑吉喻志浩
Owner JIANGSU UNIV
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