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Node centrality prediction method based on K-order Markov chain in mobile social network

A mobile social network, Markov chain technology, applied in data exchange networks, digital transmission systems, electrical components, etc., can solve problems such as performance differences, achieve strong universality, improve accuracy, and simple algorithms.

Active Publication Date: 2019-04-12
CHINA THREE GORGES UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, several intuitive and comprehensive centrality prediction methods proposed by Kim, Zhou et al. have large differences in the performance of prediction methods in data sets with different characteristics.

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  • Node centrality prediction method based on K-order Markov chain in mobile social network
  • Node centrality prediction method based on K-order Markov chain in mobile social network
  • Node centrality prediction method based on K-order Markov chain in mobile social network

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Embodiment

[0062] figure 1 An example of time window division is given. Suppose the observed network start time is T s =0, the end time is T e =T. Since the topological structure of the mobile social network changes rapidly, in order to facilitate modeling, the present invention converts the time information into a series of network "snapshots" to study the characteristics of the dynamic network. like figure 1 As shown, the present invention divides the past observation time T into n=T / w time windows according to the window size w, and the goal of the present invention is to predict the n+1th time in the future based on the data of the past n time windows The nodal centrality value of the window. The specific method is to use the contact history stored in the cache to construct a time series network topology map for each node.

[0063] like figure 2 As shown in , assuming that the observation time is 3 time windows, node A constructs a time series network topology graph based on ...

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Abstract

The invention provides a node centrality prediction method based on a K-order Markov chain in a mobile social network. For a mobile social network with N mobile nodes, wherein i belongs to {1,2, . . ., N}, in the mobile social network, the contact between the nodes is described as a network connected graph G(V, E), and a random contact process between node pairs i, j belonging to V is modeled asan edge eij belonging to E in the connected graph. It is assumed that an observed network start time is Ts=0, and the end time is Te=T. The previous observation time T is divided into n=T / w time windows according to a window size w, and a node centrality value of the future (n+1)th time window is predicted based on the data of the previous n time windows. According to the prediction method provided by the invention, the future centrality of a node is predicted from the perspective of a transient state by using a Markov chain model, and by adoption of the prediction method, the prediction accuracy can not only be improved, and the universality is higher.

Description

technical field [0001] The invention relates to the technical field of modeling and prediction of node centrality in a mobile social network, in particular to a method for predicting node centrality based on a K-order Markov chain in a mobile social network. Background technique [0002] In recent years, with the popularity and popularity of mobile smart devices equipped with Wi-Fi interfaces or Bluetooth interfaces (such as smart phones, Ipads, etc.), applications based on mobile social networks have developed vigorously. Although the mobile social network is essentially a dynamically connected network with time-varying topology, the activities of users in the network with this characteristic are not irregular movements. In mobile social networks, the mobile characteristics of nodes mainly depend on human behavior patterns, while human individual or group activities generally have the characteristics of regularity, agglomeration, and sociality. The inherent regularity of h...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04W4/21
CPCH04L41/12H04L41/142H04L41/145H04L41/147H04W4/21
Inventor 周欢陈鑫江恺吴桐
Owner CHINA THREE GORGES UNIV