scholarly journals Forecasting failure locations in 2-dimensional disordered lattices

2019 ◽  
Vol 116 (34) ◽  
pp. 16742-16749 ◽  
Author(s):  
Estelle Berthier ◽  
Mason A. Porter ◽  
Karen E. Daniels
Keyword(s):  
Betweenness Centrality ◽  
Granular Media ◽  
Shortest Paths ◽  
Structural Materials ◽  
Geometric Structures ◽  
Contact Networks ◽  
Disordered Structure ◽  
Edge Betweenness ◽  
The Mean

Forecasting fracture locations in a progressively failing disordered structure is of paramount importance when considering structural materials. We explore this issue for gradual deterioration via beam breakage of 2-dimensional (2D) disordered lattices, which we represent as networks, for various values of mean degree. We study experimental samples with geometric structures that we construct based on observed contact networks in 2D granular media. We calculate geodesic edge betweenness centrality, which helps quantify which edges are on many shortest paths in a network, to forecast the failure locations. We demonstrate for the tested samples that, for a variety of failure behaviors, failures occur predominantly at locations that have larger geodesic edge betweenness values than the mean one in the structure. Because only a small fraction of edges have values above the mean, this is a relevant diagnostic to assess failure locations. Our results demonstrate that one can consider only specific parts of a system as likely failure locations and that, with reasonable success, one can assess possible failure locations of a structure without needing to study its detailed energetic states.

scholarly journals Betweenness centrality for temporal multiplexes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Silvia Zaoli ◽  
Piero Mazzarisi ◽  
Fabrizio Lillo
Keyword(s):  
Information Flow ◽  
Real World ◽  
Betweenness Centrality ◽  
Temporal Structure ◽  
Shortest Paths ◽  
Single Layer ◽  
Distance Metric ◽  
Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

scholarly journals The Power of Quasi-Shortest Paths: $\rho$ -Geodesic Betweenness Centrality

2017 ◽  
Vol 4 (3) ◽  
pp. 187-200
Author(s):  
Dianne S. V. de Medeiros ◽  
Miguel Elias M. Campista ◽  
Nathalie Mitton ◽  
Marcelo Dias de Amorim ◽  
Guy Pujolle
Keyword(s):  
Betweenness Centrality ◽  
Shortest Paths

scholarly journals Betweenness centrality profiles in trees

2017 ◽  
Vol 5 (5) ◽  
pp. 776-794
Author(s):  
Benjamin Fish ◽  
Rahul Kushwaha ◽  
György Turán
Keyword(s):  
Betweenness Centrality ◽  
Shortest Paths ◽  
Random Trees ◽  
Experimental Results ◽  
Basic Notion ◽  
Worst Case ◽  
Scale Free ◽  
Order Of Magnitude

Abstract Betweenness centrality of a vertex in a graph measures the fraction of shortest paths going through the vertex. This is a basic notion for determining the importance of a vertex in a network. The $k$-betweenness centrality of a vertex is defined similarly, but only considers shortest paths of length at most $k$. The sequence of $k$-betweenness centralities for all possible values of $k$ forms the betweenness centrality profile of a vertex. We study properties of betweenness centrality profiles in trees. We show that for scale-free random trees, for fixed $k$, the expectation of $k$-betweenness centrality strictly decreases as the index of the vertex increases. We also analyse worst-case properties of profiles in terms of the distance of profiles from being monotone, and the number of times pairs of profiles can cross. This is related to whether $k$-betweenness centrality, for small values of $k$, may be used instead of having to consider all shortest paths. Bounds are given that are optimal in order of magnitude. We also present some experimental results for scale-free random trees.

scholarly journals Prediction of drug-protein interaction and drug repositioning using machine learning model

2020 ◽  
Author(s):  
Yu-Ting Lin ◽  
Sheh-Yi Sheu ◽  
Chen-Ching Lin
Keyword(s):  
Machine Learning ◽  
Betweenness Centrality ◽  
Drug Repositioning ◽  
Protein Network ◽  
Training Data ◽  
Support Vector ◽  
Binding Interaction ◽  
Binding Strength ◽  
Edge Betweenness ◽  
Protein Models

AbstractBackgroundTraditional drug development is time-consuming and expensive, while computer-aided drug repositioning can improve efficiency and productivity. In this study, we proposed a machine learning pipeline to predict the binding interaction between proteins and marketed or studied drugs. We then extended the predicted interactions to construct a protein network that could be applied to discover the potentially shared drugs between proteins and thus predict drug repositioning.MethodsBinding information between proteins and drugs from the Binding Database and the physicochemical properties of drugs from the ChEMBL database were used to build the machine learning models, i.e. support vector regression. We further measured proportionalities between proteins by the predicted binding affinity and introduced edge betweenness centrality to construct a protein similarity network for drug repositioning.ResultsAs the proof of concept, we demonstrated our machine learning approach is capable of reflecting the binding strength between drugs and the target protein. When comparing coefficients of protein models, we found proteins SYUA and TAU that may share common ligand which were not in our training data. Using the edge betweenness centrality network based on the prediction proportionality of protein models, we found a potential target, AK1C2, of aspirin and of which the binding interaction had been validated.ConclusionsOur study could not only be applied to drug repositioning by comparing protein models or searching the protein-protein network, but also to predict the binding strength once the sufficient experimental data was provided to train the protein models.

From paths to blocks: New measures for street patterns

2016 ◽  
Vol 44 (2) ◽  
pp. 256-271 ◽  
Author(s):  
Marc Barthelemy
Keyword(s):  
Betweenness Centrality ◽  
Spatial Organization ◽  
Shortest Paths ◽  
Urban Systems ◽  
Conditional Probability Distribution ◽  
Planar Network ◽  
Formation And Evolution ◽  
Straight Lines ◽  
The Impact ◽  
Planar Networks

The street network is an important aspect of cities and contains crucial information about their organization and evolution. Characterizing and comparing various street networks could then be helpful for a better understanding of the mechanisms governing the formation and evolution of these systems. Their characterization is however not easy: there are no simple tools to classify planar networks and most of the measures developed for complex networks are not useful when space is relevant. Here, we describe recent efforts in this direction and new methods adapted to spatial networks. We will first discuss measures based on the structure of shortest paths, among which the betweenness centrality. In particular for time-evolving road networks, we will show that the spatial distribution of the betweenness centrality is able to reveal the impact of important structural transformations. Shortest paths are however not the only relevant ones. In particular, they can be very different from those with the smallest number of turns—the simplest paths. The statistical comparison of the lengths of the shortest and simplest paths provides a nontrivial and nonlocal information about the spatial organization of planar graphs. We define the simplicity index as the average ratio of these lengths and the simplicity profile characterizes the simplicity at different scales. Measuring these quantities on artificial (roads, highways, railways) and natural networks (leaves, insect wings) show that there are fundamental differences—probably related to their different function—in the organization of urban and biological systems: there is a clear hierarchy of the lengths of straight lines in biological cases, but they are randomly distributed in urban systems. The paths are however not enough to fully characterize the spatial pattern of planar networks such as streets and roads. Another promising direction is to analyze the statistics of blocks of the planar network. More precisely, we can use the conditional probability distribution of the shape factor of blocks with a given area, and define what could constitute the fingerprint of a city. These fingerprints can then serve as a basis for a classification of cities based on their street patterns. This method applied on more than 130 cities in the world leads to four broad families of cities characterized by different abundances of blocks of a certain area and shape. This classification will be helpful for identifying dominant mechanisms governing the formation and evolution of street patterns.

scholarly journals Comparison of Multiple Random Walks Strategies for Searching Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhongtuan Zheng ◽  
Hanxing Wang ◽  
Shengguo Gao ◽  
Guoqiang Wang
Keyword(s):  
Random Walks ◽  
Random Network ◽  
Probability Distributions ◽  
First Passage Time ◽  
Shortest Paths ◽  
Passage Time ◽  
Practical Significance ◽  
First Passage ◽  
The Mean ◽  
High Degree

We investigate diverse random-walk strategies for searching networks, especially multiple random walks (MRW). We use random walks on weighted networks to establish various models of single random walks and take the order statistics approach to study corresponding MRW, which can be a general framework for understanding random walks on networks. Multiple preferential random walks (MPRW) and multiple simple random walks (MSRW) are two special types of MRW. As search strategies, MPRW prefers high-degree nodes while MSRW searches for low-degree nodes more efficiently. We analyze the first passage time (FPT) of wandering walkers of MRW and give the corresponding formulas of probability distributions and moments, and the mean first passage time (MFPT) is included. We show the convergence of the MFPT of the first arriving walker and find the MFPT of the last arriving walker closely related with the mean cover time. Simulations confirm analytical predictions and deepen discussions. We use a small random network to test the FPT properties from different aspects. We also explore some practical search-related issues by MRW, such as detecting unknown shortest paths and avoiding poor routings on networks. Our results are of practical significance for realizing optimal routing and performing efficient search on complex networks.

scholarly journals Targeted pandemic containment through identifying local contact network bottlenecks

2021 ◽  
Vol 17 (8) ◽  
pp. e1009351
Author(s):  
Shenghao Yang ◽  
Priyabrata Senapati ◽  
Di Wang ◽  
Chris T. Bauch ◽  
Kimon Fountoulakis
Keyword(s):  
Disease Transmission ◽  
Network Flow ◽  
State Of The Art ◽  
Simulation Models ◽  
Simulation Modelling ◽  
Structural Features ◽  
Contact Network ◽  
Contact Networks ◽  
Edge Betweenness ◽  
Models Of Disease

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts–between individuals or between population centres–are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.

scholarly journals Two betweenness centrality measures based on Randomized Shortest Paths

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ilkka Kivimäki ◽  
Bertrand Lebichot ◽  
Jari Saramäki ◽  
Marco Saerens
Keyword(s):  
Betweenness Centrality ◽  
Shortest Paths ◽  
Centrality Measures

Road network vulnerability to extreme floods: accessibility-based analysis and patterns of vulnerability

2021 ◽  
Author(s):  
Tsolmongerel Papilloud ◽  
Andreas Zischg ◽  
Margreth Keiler
Keyword(s):  
Vulnerability Assessment ◽  
Betweenness Centrality ◽  
Road Network ◽  
Vulnerability Analysis ◽  
Direct Impact ◽  
Contributing Factors ◽  
The Road ◽  
Flood Impacts ◽  
Edge Betweenness ◽  
Extreme Flood

<p>Understanding the different dimensions of vulnerability to floods is instrumental to gaining knowledge on flood impacts, to guide the development of appropriate risk analysis methods and to make critical decisions in risk management. Vulnerability assessment of complex systems, such as transportation infrastructure, demands an integrated framework to include various analytical methods to investigate the problem from the different characteristic perspectives related to their topological, functional, logic and dynamic properties. One approach to understand the impacts of transportation infrastructure disruptions on people is the accessibility-based vulnerability approach. Accessibility-based vulnerability analysis examines changes of access levels across a traffic network disrupted by floods, thereby providing insight on the impacts to a broader range of socio-economic aspects and to the society as a whole.</p><p>The presented study evaluates two different approaches. The first approach computes direct impacts and investigates different measures for extreme flood impacts to the road network. The second approach computes indirect impacts and</p><p>i) incorporates detailed information about the local road network in the accessibility-based vulnerability analysis by modifying the approach of calculating travel time between zones,</p><p>ii) includes additional contributing factors to the accessibility-based vulnerability analysis by considering residents and socio-economic opportunities in flood-affected areas,</p><p>iii) effectively identifies the most vulnerable traffic zones with respect to selected extreme flood scenarios, and</p><p>iv) investigates the influence of different spatial patterns of floods on accessibility-based vulnerability assessment.</p><p>We used three measures to assess direct flood impacts on the road network towards selecting the flood scenarios, which are representative for different flood patterns. Namely, Loss Index (LI), the total value of normalized edge betweenness centrality (Total-EBC), and the average normalized edge betweenness centrality (Mean-EBC). The Hansen integral accessibility approach was modified for two vulnerability indices considering traffic zones along with average shortest travel time as cost and applied for selected flood scenarios. The resulted vulnerability indices were additionally analyzed to identify the most vulnerable traffic zones for each approach and the spatial influence of the flood and network pattern as well as the distribution of population and opportunities. Finally, effects of the contributing factors to the vulnerability were investigated using correlation and comparison between the flood scenarios.</p><p>The results of the direct impact assessment show that different flood scenario and varying spatial extent are selected as extreme events based on Total-EBC and Mean-EBC. The comparisons of these different measures in assessing direct impact of extreme floods to road network allows to plan different services on disaster mitigation to place mitigation policies to be efficient. Most of the highly vulnerable traffic zones are related to the flood extent in these zones and affected population and opportunities in the traffic zones. However, the most remote traffic zones were also highly vulnerable in flood scenario, if some parts of the important connecting roads for these remote traffic zones were disturbed by a flood in traffic zones faraway. The overall results implicate those different types of flood scenarios could be classified into several groups according to their patterns of vulnerability.</p>

Sign in / Sign up

Export Citation Format

Share Document