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      Personal Trajectory with Ring Structure Network: Algorithms and Experiments

      Author(s): 1 , 1 , 1 , 2
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      Journal: Security and Communication Networks
      Publisher: Hindawi Limited

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          Abstract

          Network theory has provided a new analytical tool for the study of human trajectory and has also achieved rapid development in the complex network field. Conventional network model or complex network model ignores some details and cannot display the most remarkable features for a GPS based personal trajectory. It is necessary to set up a new personal trajectory model. For the purpose of researching the characteristics of trajectory for one person in a long time, we collected a GPS based personal LifeLog dataset named Liu Lifelog in the past 9 years. This paper analyzed the Liu Lifelog and proposed a ring structure personal trajectory (RSPT) model based on the basic complex network model. We discussed the definition, source, characteristic and attribute of the RSPT model and tested the model with the dataset which was provided by the Geolife project and verified that the model described the characteristic of trajectory for a person well. The result shows that this model is feasible and it can predict the human behavior characteristics more accurately and effectively.

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          Collective dynamics of 'small-world' networks.

          D. Watts, S Strogatz (1998)
          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Emergence of Scaling in Random Networks

            Albert-László Barabási, Réka Albert (2000)
            Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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              Community structure in social and biological networks.

              M Girvan, M. E. J. Newman (2002)
              A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
              Article 0 comments Cited 748 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Journal
                Title: Security and Communication Networks
                Abbreviated Title: Security and Communication Networks
                Publisher: Hindawi Limited
                ISSN (Electronic): 1939-0122
                ISSN (Print): 1939-0114
                Publication date Created: June 8 2021
                Publication date (Print): June 8 2021
                Volume: 2021
                Pages: 1-8
                Affiliations
                [1 ]School of Sciences, Shenyang Jianzhu University, Shenyang 110168, China
                [2 ]School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China
                Article
                DOI: 10.1155/2021/9974191
                SO-VID: 3cd94ace-2472-489e-bbde-3ef02b291927
                Copyright © © 2021
                License:

                https://creativecommons.org/licenses/by/4.0/

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