scholarly journals Transtemporal edges and crosslayer edges in incompressible high-order networks

2019 ◽  
Author(s):  
Felipe Abrahão ◽  
Klaus Wehmuth ◽  
Artur Ziviani
Keyword(s):  
Standard Deviation ◽  
Complex Networks ◽  
Theoretical Investigation ◽  
Real World ◽  
Dynamic Networks ◽  
Network Size ◽  
High Order ◽  
Topological Properties ◽  
High K ◽  
Short Diameter

This work presents some outcomes of a theoretical investigation of incompressible high-order networks defined by a generalized graph represen tation. We study some of their network topological properties and how these may be related to real world complex networks. We show that these networks have very short diameter, high k-connectivity, degrees of the order of half of the network size within a strong-asymptotically dominated standard deviation, and rigidity with respect to automorphisms. In addition, we demonstrate that incompressible dynamic (or dynamic multilayered) networks have transtemporal (or crosslayer) edges and, thus, a snapshot-like representation of dynamic networks is inaccurate for capturing the presence of such edges that compose underlying structures of some real-world networks.

scholarly journals Preprocessing Rules for Target Set Selection in Complex Networks

2020 ◽  
Author(s):  
Renato Silva Melo ◽  
André Luís Vignatti
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
Topological Properties ◽  
Input Graph ◽  
Target Set ◽  
Law Degree ◽  
Entire Network ◽  
Target Set Selection

In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.

On the growth of directed complex networks with preferential attachment: Effect upon the prohibition of multiple links

2015 ◽  
Vol 26 (06) ◽  
pp. 1550066 ◽  
Author(s):  
J. Esquivel-Gómez ◽  
R. E. Balderas-Navarro ◽  
Edgardo Ugalde ◽  
J. Acosta-Elías
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Preferential Attachment ◽  
Citation Network ◽  
Topological Properties ◽  
The World ◽  
Single Link ◽  
Paper Citation ◽  
Real Complex ◽  
Connectivity Distribution

Several real-world directed networks do not have multiple links. For example, in a paper citation network a paper does not cite two identical references, and in a network of friends there exists only a single link between two individuals. This suggest that the growth and evolution models of complex networks should take into account such feature in order to approximate the topological properties of this class of networks. The aim of this paper is to propose a growth model of directed complex networks that takes into account the prohibition of the existence multiple links. It is shown through numerical experiments that when multiple links are forbidden, the exponent γ of the in-degree connectivity distribution, [Formula: see text], takes values ranging from 1 to ∞. In particular, the proposed multi-link free (MLF) model is able to predict exponents occurring in real-world complex networks, which range 1.05 < γ < 3.51. As an example, the MLF reproduces somxe topological properties exhibited by the network of flights between airports of the world (NFAW); i.e. γ ≈ 1.74. With this result, we believe that the multiple links prohibition might be one of the local processes accounting for the existence of exponents γ < 2 found in some real complex networks.

scholarly journals Expected Emergent Algorithmic Creativity and Integration in Dynamic Complex Networks

2018 ◽  
Author(s):  
Felipe S. Abrahão ◽  
Klaus Wehmuth ◽  
Artur Ziviani
Keyword(s):  
Phase Transition ◽  
Complex Networks ◽  
Theoretical Investigation ◽  
Network Topology ◽  
Communication Protocol ◽  
Network Size ◽  
Turing Machines ◽  
Emergence Of Complexity ◽  
Unlimited Amount ◽  
Over Time

We present a theoretical investigation of the emergence of complexity or irreducible information in networked computable systems when the network topology may change over time. For this purpose, we build a network model in which nodes are randomly generated Turing machines that obey a communication protocol of imitation of the fittest neighbor. Then, we show that there are topological conditions that trigger a phase transition in which eventually these networked computable systems begin to produce an unlimited amount of bits of expected emergent algorithmic complexity, creativity and integration as the network size goes to infinity.

The impact of local processes and the prohibition of multiple links in the topological properties of directed complex networks

2015 ◽  
Vol 26 (12) ◽  
pp. 1550142 ◽  
Author(s):  
J. Esquivel-Gómez ◽  
P. D. Arjona-Villicaña ◽  
J. Acosta-Elías
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Real World ◽  
Dynamic Properties ◽  
Topological Properties ◽  
A Value ◽  
Proposed Model ◽  
Accelerated Growth ◽  
Dynamical Properties ◽  
The Impact

Local processes exert influence on the growth and evolution of complex networks, which in turn shape the topological and dynamic properties of these networks. Some local processes have been researched, for example: Addition of nodes and links, rewiring of links between nodes, accelerated growth, link removal, aging, copying and multiple links prohibition. These processes impact directly into the topological and dynamical properties of complex networks. This paper introduces a new model for growth of directed complex networks which incorporates the prohibition of multiple links, addition of nodes and links, and rewiring of links. This paper also reports on the impact that these processes have in the topological properties of the networks generated with the proposed model. Numerical simulation shows that, when the frequency of rewiring increases in the proposed model, the γ exponent of the in-degree distribution approaches a value of 1.1. When the frequency of adding new links increases, the γ exponent approaches 1. That is the proposed model is able to generate all exponent values documented in real-world networks which range 1.05 < γ < 8.94.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals All-case Japanese post-marketing surveillance of the real-world safety and efficacy of rituximab treatment in patients with refractory nephrotic syndrome

2021 ◽  
Author(s):  
Mana Kobayashi ◽  
Yutaro Kageyama ◽  
Takashi Ando ◽  
Junko Sakamoto ◽  
Shohji Kimura
Keyword(s):  
Nephrotic Syndrome ◽  
Standard Deviation ◽  
Real World ◽  
Pediatric Patients ◽  
Safety And Efficacy ◽  
The Real ◽  
Post Marketing Surveillance ◽  
Post Marketing ◽  
Refractory Nephrotic Syndrome ◽  
Observation Period

Abstract Background Rituximab is conditionally approved in Japan for use in patients with refractory nephrotic syndrome. To meet the conditions of approval, an all-case post-marketing surveillance study was conducted to confirm the real-world safety and efficacy of rituximab in patients of all ages with refractory nephrotic syndrome. Methods All patients scheduled to receive rituximab treatment for refractory nephrotic syndrome were eligible to register (registration: August 29, 2014 through April 15, 2016); the planned observation period was 2 years from the initiation of rituximab treatment (intravenous infusion, 375 mg/m2 once weekly for four doses). The study was conducted at 227 hospitals throughout Japan. Adverse drug reactions (ADRs) were collected for safety outcomes. The efficacy outcomes were relapse-free period and the degree of growth in pediatric (< 15 years) patients. Results In total, 997 (447 pediatric) patients were registered; 981 (445) were included in the safety analysis set; 852 (402) completed the 2-year observation period; and 810 (429) were included in the efficacy analysis set. Refractory nephrotic syndrome had developed in childhood for 85.0% of patients, and 54.6% were aged ≥15 years. ADRs were observed in 527 (53.7%) patients, treatment-related infection/infestation in 235 (24.0%) patients, and infusion reactions in 313 (31.9%) patients. The relapse-free period was 580 days (95% confidence interval, 511–664). There was a significant change in height standard deviation score (pediatric patients; mean change, 0.093; standard deviation, 0.637; P = 0.009). Conclusion The safety and efficacy of rituximab treatment in patients with refractory nephrotic syndrome were confirmed in the real-world setting. Clinical trial registration UMIN000014997.

scholarly journals Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 960
Author(s):  
Zhan Li ◽  
Jianhang Zhang ◽  
Ruibin Zhong ◽  
Bir Bhanu ◽  
Yuling Chen ◽  
...  
Keyword(s):  
Real World ◽  
Evaluation Criteria ◽  
Synthetic Data ◽  
Network Size ◽  
Image Dehazing ◽  
Depth Data ◽  
Network Training ◽  
Dark Channel ◽  
Processing Techniques ◽  
Transmission Estimation

In this paper, a transmission-guided lightweight neural network called TGL-Net is proposed for efficient image dehazing. Unlike most current dehazing methods that produce simulated transmission maps from depth data and haze-free images, in the proposed work, guided transmission maps are computed automatically using a filter-refined dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images. A double-error loss function that combines the errors of a transmission map with the errors of a dehazed image is used to guide network training. The method provides a feasible solution for introducing priors obtained from traditional non-learning-based image processing techniques as a guide for training deep neural networks. Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance with a much smaller network size and with significant improvements in efficiency resulting from the training guidance.

scholarly journals An Optimal and Stable Algorithm for Clustering Numerical Data

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 197
Author(s):  
Ali Seman ◽  
Azizian Mohd Sapawi
Keyword(s):  
Standard Deviation ◽  
Real World ◽  
Clustering Algorithm ◽  
Numerical Data ◽  
Zero Point ◽  
The Other ◽  
Suitable Alternative ◽  
Stable Algorithm ◽  
Real World Applications

In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtained for every run. In real-world applications, optimal and stable clustering is highly desirable. This report introduces a new clustering algorithm called the zero k-approximate modal haplotype (Zk-AMH) algorithm that uses a simple and novel seeding mechanism known as zero-point multidimensional spaces. The Zk-AMH provides cluster optimality and stability, therefore resolving the aforementioned issues. Notably, the Zk-AMH algorithm yielded identical mean scores to maximum, and minimum scores in 100 runs, producing zero standard deviation to show its stability. Additionally, when the Zk-AMH algorithm was applied to eight datasets, it achieved the highest mean scores for four datasets, produced an approximately equal score for one dataset, and yielded marginally lower scores for the other three datasets. With its optimality and stability, the Zk-AMH algorithm could be a suitable alternative for developing future clustering tools.

Motion estimation in vehicular environments based on Bayesian dynamic networks

2021 ◽  
pp. 1-12
Author(s):  
Lauro Reyes-Cocoletzi ◽  
Ivan Olmos-Pineda ◽  
J. Arturo Olvera-Lopez
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Ground Truth ◽  
Change Of Direction ◽  
Prediction Rate ◽  
Different Types ◽  
Novel Method ◽  
Comparison Of The Results ◽  
Multiple Obstacles ◽  
Real Traffic

The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work.

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