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Social network sampling method based on hybrid jump

A social network and jump technology, which is applied in the field of social network sampling based on hybrid jump, can solve problems such as local trapping, achieve good sampling effect and good diagnostic convergence.

Inactive Publication Date: 2018-08-24
ZHEJIANG SCI-TECH UNIV
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Problems solved by technology

[0004] In view of the above-mentioned deficiencies, the present invention provides a social network sampling method based on hybrid jumps, which solves the problem of partial trapping in the social network sampling process of the classic MHRW method, and obtains from the Geweke diagnosis convergence of network sampling and the distribution of sampling nodes. better effect

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  • Social network sampling method based on hybrid jump
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[0024] In order to make the above-mentioned other objects, features and advantages of the invention more obvious, further description will be given below in conjunction with specific embodiments.

[0025] An OSNs are usually modeled as a social graph with a set of nodes, with user-to-user relationships as edges of the graph. Here, the social network graph is defined as an undirected and unweighted graph G=(V,E). Among them, each vertex v in the set V represents a user in the OSNs, and the total number of nodes is |V|=n. Each edge e in the set E represents a friendship relationship between users, and the total number of edges is |E|=m. (v,w) can be used to represent an edge in a complex network, v,w∈V,w is the neighbor node of v, the neighbor node set of v is denoted as V={w|(v,w), and k w represents the degree of node w. Q vw Defined here as the degree ratio, i.e. Q vw =k v / k w . Set the initial node for crawling to u. S is a smaller set of subnodes similar to the o...

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Abstract

The present invention discloses a social network sampling method based on hybrid jump. In the social network sampling, the method is based on a current classic MHRW (Metropolis-Hasting Random Walk) sampling method and employs a random jump strategy to prevent sampling from falling into a local sub network to effectively solve the large-scale complex social network sampling problem so as to obtainunbiased social network sample set data. The method provided by the invention combines a BFS (Breath-first Search) method at the first time to have fast sampling speed and have no duplicate nodes in the samples, and employs a cubic spline interpolation method to establish a three-dimensional average degree distribution model to determine a jump parameter optimization value of a social network sampling method. The method can better provide guidance for selection of graph sampling setting parameters to allow the sampling method to achieve the best sampling effect. The social network sampling method based on hybrid jump provides a new idea for a social network sampling method so as to facilitate performance research of the large-scale complex social network.

Description

technical field [0001] The invention relates to the technical field of social network data sampling, in particular to a hybrid jump-based social network sampling method (Hybrid Jump Sampling, HJ Sampling). Background technique [0002] In recent years, social networks such as Facebook and Twitter are becoming an indispensable part of life, so that the Internet-based social media is affecting and changing our lives. According to the "DIGITAL IN 2017 GLOBALOVERVIEW" report, the total number of users of various social networks in the world in 2017 was 3.028 billion, and the total population of the world is currently 7.5 billion. This means that mobile Internet users account for the vast majority of social network users, 40% of the world's population uses social networks, and the total number of users is still growing. Among them, Facebook, one of the most popular online social networks today, has exceeded 2 billion users worldwide (as of July 2017). In addition, Twitter's glo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 刘良桂王玲敏贾会玲张宇
Owner ZHEJIANG SCI-TECH UNIV
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