Characterizing traffic time series based on complex network theory

Tang, Jinjun; Wang, Yinhai; Liu, Fang

doi:10.1016/j.physa.2013.05.012

Cited by 70 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The network based theories and methodologies have been applied in many disciplines such as biology, sociology, physics, climatology, and neurosciences. [7][8][9][10][11]14,15 Gao and Jin 8 show that the idea of complex network analysis is able to identify flow patterns of gas-liquid two phase flows. Also, Liu et al 16 analyzed time series of energy dissipation rates in three-dimensional fully developed turbulence using the visibility algorithm.…”

Section: Introductionmentioning

confidence: 99%

The application of complex network time series analysis in turbulent heated jets

Charakopoulos¹,

Karakasidis²,

Papanicolaou³

et al. 2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.

show abstract

Section: Introductionmentioning

confidence: 99%

The application of complex network time series analysis in turbulent heated jets

Charakopoulos¹,

Karakasidis²,

Papanicolaou³

et al. 2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

“…[17], we can derive 1 (9). In order to reflect the effect of the controller proposed in this paper, the uncontrolled error states of the system are depicted in Fig.…”

Section: Individual Synchronization By Hybrid Controller With Static mentioning

confidence: 99%

Individual synchronization control of complex network with nonidentical nodes

Zhang

Yan

Yang

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

View full text Add to dashboard Cite

“…In this paper, we introduce complex network theory to present the complex interaction behavior of large-scale network traffic flows. Since complex network provides a powerful mechanism for capturing the interactive relationships among study objects, it has been an effective method for relational expression of structured datasets [4,5,6], especially the time series data. For instance, in the study of earthquake time series the authors [7,8] developed the earthquake complex network model based on time influence domain, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…For the road traffic, Zheng et al [9] proposed a simple weighted network model which presents the similarities of traffic flow states by defining the traffic flow state as network node. However Tang et al used complex network theory to study the similarities of sampling points on time series [10,11,12], and to study the complexity of network states extracted from traffic flow time series [4,13]. Zanin [14] constructed the complex network representing of air traffic flows to identify the situations in which probability of appearance of Loss of Separation events is increased.…”

Section: Introductionmentioning

confidence: 99%

Using complex network theory for temporal locality in network traffic flows

Wang

et al. 2019

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Monitoring the interaction behaviors of network traffic flows and detecting unwanted Internet applications and anomalous flows have become a challenging problem, since many applications obfuscate their network traffic flow using unregistered port numbers or payload encryption. In this paper, the temporal locality complex network model-TLCN is proposed as a way to monitor, analyze and visualize network traffic flows. TLCNs model the interaction behaviors of large-scale network traffic flows, where the nodes and the edges can be defined to represent different flow levels and flow interactions separately. Then, the statistical characteristics and dynamic behaviors of the TLCNs are studied to represent TLCN's structure representing ability to the flow interactions. According to the analysis of TLCN statistical characteristics with different Internet applications, we found that the weak interaction flows prefer to form the small-world TLCN and the strong interaction flows prefer to the scale-free TLCN. In the studies of anomaly behaviors of TLCNs, the network structure of attacked TLCNs can have a remarkable feature for three attack patterns, and the evolution of TLCNs exhibits a good consistency between TLCN structure and attack process. With the introduction of TLCNs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.

show abstract

Characterizing traffic time series based on complex network theory

Cited by 70 publications

References 24 publications

The application of complex network time series analysis in turbulent heated jets

The application of complex network time series analysis in turbulent heated jets

Individual synchronization control of complex network with nonidentical nodes

Using complex network theory for temporal locality in network traffic flows

Contact Info

Product

Resources

About