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Generalized maximum degree random walk graph sampling algorithm

A random walk, sampling algorithm technology, applied in computing, data processing applications, instruments, etc., can solve the problem of poor estimation accuracy of sampling algorithm, aggravating the problem of repeated samples, etc.

Inactive Publication Date: 2015-03-25
SHENZHEN UNIV
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  • Abstract
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Problems solved by technology

The larger the chi-square distance between the two, the worse the estimation accuracy of the sampling algorithm
Obviously, this approach will lead to more self-loops, thus exacerbating the "repeated sample problem"

Method used

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  • Generalized maximum degree random walk graph sampling algorithm
  • Generalized maximum degree random walk graph sampling algorithm
  • Generalized maximum degree random walk graph sampling algorithm

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Embodiment Construction

[0019] Specific embodiments of the present invention will be described in detail below in conjunction with specific drawings.

[0020] The present invention provides a new generalized maximum degree random walk algorithm, hereinafter referred to as 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, samples are collected by random walk on the graph through the above-mentioned transition probability; secondly, an unbiased estimate is constructed 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,...

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Abstract

The invention discloses a generalized maximum degree random walk graph sampling algorithm. The generalized maximum degree random walk graph sampling algorithm comprises the following steps of enabling a sample to walk on a graph randomly; and performing unbiased estimation according to the sample. A 'large deviation problem' of an RW algorithm and a 'repeated sample problem' of an MD algorithm can be solved effectively, so that the overall efficiency on sample points acquired from the internet is improved.

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 algorithm. Background technique [0002] In recent years, online social network analysis has attracted extensive attention in both academia and industry. In all related studies of 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 entire social network. However, since many online social network companies, such as Tencent, Sina Weibo, Facebook, and Twitter, do not release their social network graph data to third parties, and the size of the entire social graph data is often too much for third parties. Unknown. Therefore, the majority of researchers and developers engaged in social network analysis are faced with a very difficult data collection problem. The main difficulty here is...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/00G06F16/9024
Inventor 李荣华邱宇轩毛睿秦璐金檀蔡涛涛
Owner SHENZHEN UNIV
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