scholarly journals Maximising the clustering coefficient of networks and the effects on habitat network robustness

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0240940
Author(s):  
Henriette Heer ◽  
Lucas Streib ◽  
Ralf B. Schäfer ◽  
Stefan Ruzika
Keyword(s):  
Clustering Coefficient ◽  
Network Robustness ◽  
Habitat Network

scholarly journals Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaolong Deng ◽  
Hao Ding ◽  
Yong Chen ◽  
Cai Chen ◽  
Tiejun Lv
Keyword(s):  
Social Network ◽  
Vulnerability Assessment ◽  
Online Social Networks ◽  
Information Transfer ◽  
Large Scale ◽  
Real Life ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Directed Network ◽  
Online Social Media

In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and I M p v (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for I M p v . Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and I M p v based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of I M p v . To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.

scholarly journals Analysis of Network Robustness in Weighted and Unweighted Approaches: A Case Study of the Air Transport Network in the Belt and Road Region

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lu Zhang ◽  
Yannan Zhao ◽  
Dongli Chen ◽  
Xinhuan Zhang
Keyword(s):  
Global Economy ◽  
Transport Network ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Air Transport ◽  
Degree Centrality ◽  
Study Results ◽  
Belt And Road ◽  
The Belt And Road

Aviation transport is one of the most important and critical infrastructures in today’s global economy. Failure in its proper operations can seriously impact regional economic development, which is why it is important to evaluate network robustness. Previous analyses of robustness have mainly been conducted with an unweighted approach. In the development of air transport, however, the demand for route configuration has gradually decreased, while the demand for flight adjustments has increased. Consequently, the aviation network has developed unevenly, so adhering to a uniform approach for evaluating network robustness may lead to inaccurate results. Therefore, we examined which centrality sequence is the most sensitive to network robustness in both unweighted and weighted approaches. The air transport network selected for the case study comprised the six subregions of the Eurasian landmass of the Belt and Road region. The study results showed the following: (a) in the network constructed as an unweighted one, betweenness, and degree centrality had higher priorities in preserving network functionalities than eigenvector and closeness centrality; (b) in the network constructed as a weighted one, recursive power had a higher priority in preserving network functionalities than recursive centrality; and (c) no particular centrality measurement had a significant advantage in representing the totality of robustness. The betweenness centrality sequence was sensitive to the average shortest path length and global efficiency; the recursive power sequence was sensitive to the clustering coefficient, while degree centrality was sensitive to graph diversity. The findings of this study support the decisions about managing air transportation in the Belt and Road region.

Characterizing the robustness of power-law networks that experience spatially-correlated failures

2020 ◽  
pp. 1748006X2097447
Author(s):  
Caroline A Johnson ◽  
Allison C Reilly ◽  
Roger Flage ◽  
Seth D Guikema
Keyword(s):  
Standard Deviation ◽  
Relative Size ◽  
Influential Factors ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Connected Subgraph ◽  
Topological Properties ◽  
Spatially Correlated ◽  
Spatial Properties ◽  
Correlated Failures

Knowing the ability of networked infrastructure to maintain operability following a spatially distributed hazard (e.g. an earthquake or a hurricane) is paramount to managing risk and planning for recovery. Leveraging topological properties of the network, along with characteristics of the hazard field, may be an expedient way of predicting network robustness compared to more computationally-intensive simulation methods. Prior work has shown that the topological properties are insightful for predicting robustness, considered here to be measured by the relative size of the largest connected subgraph after failures, especially for networks experiencing random failures. While this does not equate to full engineering-based performance, it does provide an indication of the robustness of the network. In this work, we consider the effect that spatially-correlated failures have on network robustness using only spatial properties of the hazard and topological properties of networks. The results show that the spatial properties of the hazard together with the mean nodal degree, mean clustering coefficient, clustering coefficient standard deviation and path length standard deviation are the most influential factors in characterizing the network robustness. Using the results, recommendations are made for infrastructure management/owners to consider when improving existing systems, or designing new infrastructure. Recommendations include examining the known possible locations of potential hazards in relation to the system and considering the level of redundancy within the system.

Testing early-warning signals for the transition of ecological network properties in wetland complex

2020 ◽  
Author(s):  
Bin Kim ◽  
Hyojeong Lee ◽  
Khawon Lee ◽  
Jeryang Park
Keyword(s):  
Early Warning ◽  
Human Activities ◽  
Land Development ◽  
Warning Signal ◽  
Climatic Conditions ◽  
Ecological Networks ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Network Characteristics ◽  
Early Warning Signal

<p>Wetlands, which exist in both natural and man-made landscapes, play a critical role in providing various ecosystem services for both ecology and human-being. These services are affected not only by regional hydro-climatic and geologic conditions but also by human activities. On a landscape scale, wetlands form a complex spatial structure by their spatial distribution in a specific geological setting. Consequently, dispersal of inhabiting species between spatially distributed wetlands organizes ecological networks that are consisted of nodes (wetlands) and links (pathways of movement). In this study, we generated and analyzed the ecological networks by introducing deterministic (e.g., threshold distance) or stochastic (e.g., exponential kernel and heavy-tailed model) dispersal models. From these networks, we evaluated structural or functional characteristics including degree, efficiency, and clustering coefficient, all of which are affected by disturbances such as seasonal hydro-climatic conditions that change wetland surface area, and shocks that may remove nodes from the network (e.g., human activities for land development). Specifically, by using the characteristics of the corresponding ecological networks, we analyzed (1) their network robustness by simulating the removal of nodes selected by their degree or area; and (2) the change of variance as the early-warning signal to predict where critical point may occur in global network characteristics affected by disturbances. The results showed that there was not a clear relationship between network robustness and wetland size for node removal. However, when nodes were removed in the order of degree, the network fragmented rapidly. Also, we observed that the variance of network characteristics in the time-series increased in drier hydro-climatic conditions for all the three network models we tested. This result indicates a possibility of using increasing variance as the early-warning signal for detecting a critical transition in network characteristics as the hydro-climatic condition becomes dry. In sum, the observed characteristics of ecological networks are vulnerable to target attack on hubs (structurally important nodes) or drought. Also, the resilience of a wetlandscape can be low after hubs were destroyed or in a dry season causing the fragmentation of habitats. Implications of these results for modeling ecological networks depending on hydrologic systems and influenced by human activities will provide a new decision-making process, especially for restoring and conservation purposes.</p>

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

Random graphs

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Large Scale ◽  
Random Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Random Graph Model ◽  
Definition Of ◽  
Average Size

An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a discussion of some of the shortcomings of the random graph model.

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 Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3172
Author(s):  
Suchat Tachaudomdach ◽  
Auttawit Upayokin ◽  
Nopadon Kronprasert ◽  
Kriangkrai Arunotayanun
Keyword(s):  
Road Network ◽  
Critical Infrastructure ◽  
Policy Issue ◽  
Analytical Framework ◽  
Transportation Systems ◽  
Network Robustness ◽  
System Response ◽  
Chiang Mai ◽  
Protection Measures ◽  
Response And Recovery

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: 1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), 2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, 3) determining recovery thresholds based on land use and system functionality, and 4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems.

scholarly journals Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 970
Author(s):  
Maedeh Khalilian ◽  
Kamran Kazemi ◽  
Mahshid Fouladivanda ◽  
Malek Makki ◽  
Mohammad Sadegh Helfroush ◽  
...  
Keyword(s):  
Human Brain ◽  
Clustering Coefficient ◽  
Quality Data ◽  
B Values ◽  
Rich Club ◽  
Global Efficiency ◽  
Acquisition Strategy ◽  
The Rich ◽  
Structural Networks ◽  
Single Shell

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

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