A Generalized Maximum Degree Random Walk Graph Sampling Method

A random walk and maximum degree technology, applied in data processing applications, instruments, calculations, etc., can solve the problems of poor estimation accuracy of sampling algorithms and aggravate the problem of repeated samples

Inactive Publication Date: 2018-06-05
SHENZHEN UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

The larger the chi-square distance between the two, the worse the estimation accuracy of the sampling algorithm
Clearly, this approach leads to more self-loops, which exacerbates the "repeated sample problem"

Method used

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  • A Generalized Maximum Degree Random Walk Graph Sampling Method
  • A Generalized Maximum Degree Random Walk Graph Sampling Method
  • A Generalized Maximum Degree Random Walk Graph Sampling Method

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Embodiment Construction

[0019] The specific embodiments of the present invention will be described in detail below with reference to the specific drawings.

[0020] The present invention provides a new generalized maximum degree random walk algorithm, hereinafter referred to as the GMD algorithm.

[0021] The GMD algorithm introduces a parameter C (C is a non-negative integer) on top of the MD algorithm to control the number of self-loops. Its probability transition equation is as follows:

[0022]

[0023] where C is a non-negative integer.

[0024] Specifically, the GMD algorithm includes two steps: firstly, collecting samples by random walk on the graph through the above transition probability; secondly, constructing an unbiased estimate according to the collected samples. Among them, the detailed process of the first step is as follows:

[0025] Input: graph G = (V, E)

[0026] Output: The collected sample point set S

[0027] 1 Randomly select node u in the graph as the initial node, and ...

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Abstract

A generalized maximum-degree random walk graph sampling algorithm, comprising: random walking on a graph to acquire a sample; and structuring an unbiased estimation according to the acquired sample. The algorithm can effectively balance a large deviation problem of an RW algorithm and a sample repetition problem of an MD algorithm, thus improving the overall efficiency of acquiring sample points from a network.

Description

technical field [0001] The invention belongs to the technical field of large graph data mining, and in particular relates to a generalized maximum degree random walk graph sampling method. Background technique [0002] In recent years, online social network analysis has attracted a lot of attention in both academia and industry. In all related researches on online social network analysis, one of the most basic research problems is to estimate the properties of nodes in social networks and the topological properties of the whole social network. However, because many online social network companies, such as Tencent, Sina Weibo, Facebook, and Twitter, have not released their social network graph data to third parties, and the size of the entire social graph data is often the same for third parties. Unknown. Therefore, most researchers and developers engaged in social network analysis are faced with a very difficult data collection problem. The main difficulty here is how to ...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/00
Inventor李荣华邱宇轩毛睿秦璐金檀蔡涛涛
OwnerSHENZHEN UNIV