Sampling-based method for maximizing influence under linear threshold value model

A threshold model and influence technology, applied in the field of social network science, can solve the problems of high space complexity and high time complexity, and achieve the effects of high flexibility, improved reusability, and fast calculation speed

Inactive Publication Date: 2017-07-21
YANGZHOU UNIV
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Problems solved by technology

[0008] The technical problem solved by the present invention is to provide a method for maximizing the influence under the linear threshold model based on sampling, and propose a node influence index Pr(u), which can be calculated at one time when facing seed sets of di

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  • Sampling-based method for maximizing influence under linear threshold value model
  • Sampling-based method for maximizing influence under linear threshold value model
  • Sampling-based method for maximizing influence under linear threshold value model

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Embodiment Construction

[0022] to combine figure 1 , a method for maximizing influence based on a sampling linear threshold model of the present invention, comprising the following steps:

[0023] Step 1. Calculate the set W(G) of all possible worlds G′ of the directed graph G according to the influence of one node on the other node in the directed graph (on the edge),

[0024] The value of the influence of node u on node v in the directed graph is stored on the edge (u, v) between 0 and 1. The larger the value, the greater the influence between nodes, and the easier it is for node v to be affected by u. Where (u, v) is taken as the probability of the existence of a directed edge between u and v, and the set W(G) of all possible worlds can be obtained by using the Monto-Carlo method for multiple simulations;

[0025] Step 2. Calculate the activation probability of the path: including the probability I(s,v,G′) of the existence of the path and the probability Pr(G′) of the possible world:

[0026] 2...

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Abstract

The present invention discloses a sampling-based method for maximizing influence under a linear threshold value model. The method includes the following steps that: the sets W(G) of all possible worlds G' of a directed digraph G are calculated according to the influence of one node on another node in the directed digraph; the activation probabilities of paths are calculated, wherein the activation probabilities include the probabilities I (s, v, G') of the existence of the paths, and the probabilities Pr (G') of the possible worlds; the number r of post-sampling samples is obtained through using Chernoff bound and set parameters; and an unbiased sampling set U (G') is selected in the possible world set W (G) according to the number r of the post-sampling samples; the influence intensity function sigma (s) of the seed set s of each node v in the directed digraph G under each possible world in the unbiased sampling set U (G') is calculated; and the influence index Pr (u) and final influence intensity function sigma (s) of each node u are calculated, and the seed set s is determined according to the number of seed nodes. The method has higher calculation speed and improved reusability.

Description

technical field [0001] The invention belongs to the field of social network science, in particular to an influence maximization method based on a sampling linear threshold model. Background technique [0002] There is a relationship of mutual influence between individuals and individuals, groups and individuals, for example, the behavior of individuals depending on the group is beneficial to hunting or reducing the possibility of being caught. Human beings are advanced social animals with complex means of communication, and social influence is ubiquitous in social life. From listening to music to political opinions, our decisions are deeply influenced by friends and relatives. An in-depth understanding of the generation and dissemination modes of influence helps to understand the behavior of human individuals and groups, and then can predict people's behavior and provide reliable basis for governments, institutions, enterprises and other departments. [0003] In the field ...

Claims

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Application Information

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IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 陈崚贾苏
Owner YANGZHOU UNIV
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