scholarly journals Inferring network properties based on the epidemic prevalence

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Long Ma ◽  
Qiang Liu ◽  
Piet Van Mieghem
Keyword(s):  
Time Series ◽  
Simulated Annealing ◽  
Level Of Detail ◽  
Dynamical Process ◽  
Underlying Network ◽  
Average Fraction ◽  
Network Properties ◽  
Open Question ◽  
Network Metrics ◽  
Network Type

Abstract Dynamical processes running on different networks behave differently, which makes the reconstruction of the underlying network from dynamical observations possible. However, to what level of detail the network properties can be determined from incomplete measurements of the dynamical process is still an open question. In this paper, we focus on the problem of inferring the properties of the underlying network from the dynamics of a susceptible-infected-susceptible epidemic and we assume that only a time series of the epidemic prevalence, i.e., the average fraction of infected nodes, is given. We find that some of the network metrics, namely those that are sensitive to the epidemic prevalence, can be roughly inferred if the network type is known. A simulated annealing link-rewiring algorithm, called SARA, is proposed to obtain an optimized network whose prevalence is close to the benchmark. The output of the algorithm is applied to classify the network types.

Characterization and Coarsening of Autonomous System Networks

2016 ◽  
pp. 148-179 ◽  
Author(s):  
Alberto Garcia-Robledo ◽  
Arturo Diaz-Perez ◽  
Guillermo Morales-Luna
Keyword(s):  
Experimental Study ◽  
Complex Network ◽  
Autonomous System ◽  
The Internet ◽  
Linear Relationships ◽  
Network Properties ◽  
Network Metrics ◽  
Graph Coarsening

This Chapter studies the correlations among well-known complex network metrics and presents techniques to coarse the topology of the Internet at the Autonomous System (AS) level. We present an experimental study on the linear relationships between a rich set of complex network metrics, to methodologically select a subset of non-redundant and potentially independent metrics that explain different aspects of the topology of the Internet. Then, the selected metrics are used to evaluate graph coarsening algorithms to reduce the topology of AS networks. The presented coarsening algorithms exploit the k-core decomposition of graphs to preserve relevant complex network properties.

A Method for Scalable Real-Time Network Performance Baselining, Anomaly Detection, and Forecasting

2012 ◽  
Vol 3 (2) ◽  
pp. 13-33
Author(s):  
Robert Strahan
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Performance Management ◽  
Network Performance ◽  
Anomalous Behavior ◽  
Management Systems ◽  
Smoothing Technique ◽  
Performance Management Systems ◽  
Exponential Smoothing Technique ◽  
Network Metrics

Communication is the lifeblood of any business. Today, communication is predominantly facilitated by digital packets transported over the interconnected arteries of the data network infrastructure. It is imperative that this infrastructure is well managed, that unexpected behavior is quickly identified and explained, and that problems are predicted and preempted. Therefore, network performance management systems should be able to detect unusual or anomalous behavior as it happens, and quickly trigger automatic analysis or alert a human operator. Growth trends in network traffic must also be identified so that future problems may be anticipated and prevented. To meet these challenges, this paper proposes an integrated, scalable method to perform baselining, anomaly detection, and forecasting on time series network metrics. The method is based on the popular Holt-Winters triple exponential smoothing technique – a technique that compares favorably to other more complex and costly approaches.

Inference On Streamflow Predictability Using Horizontal Visibility Graph Based Networks

2020 ◽  
Author(s):  
Ganesh Ghimire ◽  
Navid Jadidoleslam ◽  
Witold Krajewski ◽  
Anastasios Tsonis
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Temporal Trends ◽  
Characteristic Exponent ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Series Data ◽  
Dynamical Process ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

<p>Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, λ of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of λ and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.</p>

scholarly journals Conspiracy of Corporate Networks in Corruption Scandals

2021 ◽  
Vol 9 ◽  
Author(s):  
J. R. Nicolás-Carlock ◽  
I. Luna-Pla
Keyword(s):  
Organized Crime ◽  
Public Procurement ◽  
Bipartite Network ◽  
Corporate Ownership ◽  
Corporate Networks ◽  
State Institutions ◽  
The Public ◽  
Public Resources ◽  
Network Properties ◽  
Network Metrics

Corruption in public procurement transforms state institutions into private entities where public resources get diverted for the benefit of a few. On this matter, much of the discussion centers on the legal fulfillment of the procurement process, while there are fewer formal analyses related to the corporate features which are most likely to signal organized crime and corruption. The lack of systematic evidence on this subject has the potential to bias our understanding of corruption, making it overly focused on the public sector. Nevertheless, corruption scandals worldwide tell of the importance of taking a better look at the misuse and abuse of corporations for corrupt purposes. In this context, the research presented here seeks to contribute to the understanding of the criminal conspiracy of companies involved in public procurement corruption scandals under a network and complexity science perspective. To that end, we make use of a unique dataset of the corporate ownership and management information of four important and recently documented cases of corruption in Mexico, where hundreds of companies were used to embezzle billions of dollars. Under a bipartite network approach, we explore the relations between companies and their personnel (shareholders, legal representatives, administrators, and commissioners) in order to characterize their static and dynamic networked structure. In terms of organized crime and using different network properties, we describe how these companies connect with each other due to the existence of shared personnel with role multiplicity, leading to very different conspiracy networks. To best quantify this behavior, we introduce a heuristic network-based conspiracy indicator that together with other network metrics describes the differences and similarities among the networks associated with each corruption case. Finally, we discuss some public policy elements that might be needed to be considered in anti-corruption efforts related to corporate organized crime.

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