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Influence maximization parallel accelerating method based on graphic processing unit

A graphics processing unit, a technology that maximizes impact, is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., and can solve problems such as reducing running time

Inactive Publication Date: 2012-12-12
NAT UNIV OF DEFENSE TECH
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AI Technical Summary

Problems solved by technology

There is no published literature in the existing research on the impact maximization problem that deals with the method of using the parallel computing power of the GPU to reduce the running time

Method used

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  • Influence maximization parallel accelerating method based on graphic processing unit
  • Influence maximization parallel accelerating method based on graphic processing unit

Examples

Experimental program
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Embodiment Construction

[0054]figure 1 It is the optimal greedy algorithm MixGreedy flowchart.

[0055] Step 1: Initialize the node set S to be empty.

[0056] Step 2: Set the current Monte Carlo simulation times Num=0.

[0057] Step 3: Use the Monte Carlo simulation method to select the edges of the graph to obtain the graph G′.

[0058] Step 4: Perform a breadth-first search for each node, and calculate the influence value of each node.

[0059] Step 5: Add 1 to the number of Monte Carlo simulation times Num. Determine whether Num is less than R, if Num

[0060] Step 6: Select the node v with the largest influence value of TotallnF[] in the set V-S and add it to the set S.

[0061] Step 7: If the number of nodes in the set S |S|

[0062] figure 2 It is an overall flow chart of the present invention.

[006...

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Abstract

The invention discloses an influence maximization parallel accelerating method based on a graphic processing unit. The purpose of the invention is to provide the influence maximization parallel accelerating method based on the GPU (graphic processing unit). Algorithm implementation is accelerated and the implementation time is shortened by parallel calculating ability of the GPU. The influence maximization parallel accelerating method is characterized by comprising the following steps: in each Monte Carlo simulation, firstly, finding out strong connectivity in a network diagram, merging all nodes in the same strong connectivity into a node, wherein the weight is the sum of the weights of all nodes in the strong connectivity; then calculating an influence value of each node in parallel by a strategy of traversing upwards from the bottom; using different threads by the GPU calculation cores to calculate in a parallel way the influence values of different nodes with the help of the parallel calculation capability of the GPU, and obtaining the K most influential nodes. According to the invention, a pattern is converted into a directed acyclic graph; the calculation quantity of an influence value can be obviously reduced, meanwhile, the overall operation time is shortened by scheduling parallel calculation of each node in the calculation core of the GPU to the maximal extent.

Description

technical field [0001] The invention relates to a solution to the problem of social network influence maximization in the field of massive data mining, in particular to a parallel acceleration method based on a graphics processing unit GPU proposed for massive user mining of a large-scale social network. Background technique [0002] The rapid development of Web2.0 technology has promoted the vigorous development of social media. Various social networking sites continue to emerge, such as foreign Facebook, Twitter and domestic Renren, Sina Weibo and other sites, the number of users is growing rapidly, and the current number of active Facebook users has exceeded 850 million. Social networking sites are not only a bridge for people to communicate and communicate, but also an important medium for information dissemination and diffusion. Research shows that 68% of customers will ask their family and friends for their opinions before buying a product. Viral Marketing uses the p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 李姗姗廖湘科刘晓东吴庆波戴华东彭绍亮王蕾付松龄鲁晓佩郑思
Owner NAT UNIV OF DEFENSE TECH
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