scholarly journals The Structure and Periodicity of the Chinese Air Passenger Network

2018 ◽  
Vol 11 (1) ◽  
pp. 54
Author(s):  
Hongqi Li ◽  
Haotian Wang ◽  
Ming Bai ◽  
Bin Duan
Keyword(s):  
Time Scale ◽  
Network Performance ◽  
Structural Characteristics ◽  
Small World ◽  
Air Transportation ◽  
Structural Features ◽  
Power Law Distribution ◽  
Scale Free ◽  
Temporal Features ◽  
Scale Interval

China’s air transportation system is evolving with its own unique mechanism. In particular, the structural features of the Chinese air passenger network (CAPN) are of interest. This paper aims to analyze the CAPN from holistic and microcosmic perspectives. Considering that the topological structure and the capacity (i.e., available passenger-seats) flow are important to the air network’s performance, the CAPN structure features from non-weighted and weighted perspectives are analyzed. Subnets extracted by time-scale constraints of one day or every two-hours are used to find the temporal features. This paper provides some valuable conclusions about the structural characteristics and temporal features of the CAPN. The results indicate that the CAPN has a small-world and scale-free structure. The cumulative degree distribution of the CAPN follows a two-regime power-law distribution. The CAPN tends to be disassortative. Some important airports, including national air-hubs and local air-hubs, remarkably affect the CAPN. About 90% of large capacities exist between airports with large degrees. The properties of CAPN subnets extracted by taking two hours as the time-scale interval shed light on the air network performance and the changing rule more accurately and microcosmically. The method of the spectral destiny estimation is used to find the implicit periodicity mathematically. For most indicators, a one-day cycle, two-day cycle, and/or three-day cycle can be found.

A Small-World Model of Scale-Free Networks: Features and Verifications

2011 ◽  
Vol 50-51 ◽  
pp. 166-170 ◽  
Author(s):  
Wen Jun Xiao ◽  
Shi Zhong Jiang ◽  
Guan Rong Chen
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Degree Sequence ◽  
Small World ◽  
Static Condition ◽  
Vertex Degree ◽  
Power Law Distribution ◽  
Least Common Multiple ◽  
Scale Free ◽  
Vertex Degrees

It is now well known that many large-sized complex networks obey a scale-free power-law vertex-degree distribution. Here, we show that when the vertex degrees of a large-sized network follow a scale-free power-law distribution with exponent  2, the number of degree-1 vertices, if nonzero, is of order N and the average degree is of order lower than log N, where N is the size of the network. Furthermore, we show that the number of degree-1 vertices is divisible by the least common multiple of , , . . ., , and l is less than log N, where l = < is the vertex-degree sequence of the network. The method we developed here relies only on a static condition, which can be easily verified, and we have verified it by a large number of real complex networks.

scholarly journals An interolog-based barley interactome as an integration framework for immune signaling

2021 ◽  
Author(s):  
Valeria Velásquez-Zapata ◽  
James Mitch Elmore ◽  
Gregory Fuerst ◽  
Roger Wise
Keyword(s):  
Powdery Mildew ◽  
Cellular Response ◽  
Time Course ◽  
Small World ◽  
Power Law Distribution ◽  
Integration Framework ◽  
Scale Free ◽  
Receptor Localization ◽  
Recognition Specificity ◽  
Barley Protein

The barley MLA nucleotide-binding, leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many cereal diseases, including powdery mildew, stem and stripe rust, Victoria blight, and rice blast. We used interolog inference to construct a barley protein interactome (HvInt) comprising 66133 edges and 7181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared to the experimentally validated Arabidopsis interactome of 11253 proteins and 73960 interactions, verifying that the two networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific 'omics' datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and an infection-time-course transcriptome, we assembled resistant (R) and susceptible (S) subnetworks. The resulting differentially co-expressed (R-S) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to Mla through trans eQTL associations. Lastly, next-generation, yeast-two-hybrid screens identified fifteen novel MLA interactors, which were incorporated into HvInt, to predict receptor localization, and signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.

Robustness paradox of attack behavior induced by cascading failures: The larger the attack, the more vulnerable the network?

2020 ◽  
pp. 2150033
Author(s):  
Jianwei Wang ◽  
Yuxin Guo ◽  
Wei Kai
Keyword(s):  
Network Performance ◽  
Dynamic Network ◽  
Small World ◽  
Cascading Failure ◽  
Attack Behavior ◽  
Edge Deletion ◽  
Scale Free ◽  
Free Network ◽  
Random Failures ◽  
Real Traffic

The robustness of complex networks responding to attacks has long been the focus of network science researching. Nonetheless, the precious studies mostly focus on network performance when facing malicious attacks and random failures while rarely pay attention to the influences of scales of attacking. It is wondering if it is an actual fact that the network is more fragile when attacking scale is exacerbated. In this paper, we are committed to exploring the influences related to the very factor of attacking scale from the perspective of cascading failure problem of dynamic network theory. We construct the model with a regular ranking edge deletion method by simulating attacking scale with [Formula: see text] and [Formula: see text] is denoted as attacked edge number. To be specific, we rank the edges according to initial distributed loads and delete edges in the ranked list, and subsequently observe the changes of robustness in the networks, including BA scale-free network, WS small-world network and several real traffic networks. During the process, an unusual counterintuitive phenomenon captures our attention that the network damages caused by attacks do not always grow with the increase of attacked edges number. We specifically demonstrate and analyze this abnormal cascading propagation phenomenon, ascribing this paradox to the dynamics of the load and the connections of the network structure. Our work may offer a new angle on better controlling the spread of cascading failure and remind the importance of effectively protecting networks from underlying dangers.

scholarly journals Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Cao ◽  
Xiangnan Feng ◽  
Jianmin Jia ◽  
Hong Zhang
Keyword(s):  
Structural Characteristics ◽  
Flow Distribution ◽  
Small World ◽  
Centrality Measures ◽  
Railway Network ◽  
Weighted Network ◽  
Scale Free ◽  
Topological Measures ◽  
Free Network

Understanding the structure of the Chinese railway network (CRN) is crucial for maintaining its efficiency and planning its future development. To advance our knowledge of CRN, we modeled CRN as a complex weighted network and explored the structural characteristics of the network via statistical evaluations and spatial analysis. Our results show CRN as a small-world network whose train flow obeys power-law decaying, demonstrating that CRN is a mature transportation infrastructure with a scale-free structure. CRN also shows significant spatial heterogeneity and hierarchy in its regionally uneven train flow distribution. We then examined the nodal centralities of CRN using four topological measures: degree, strength, betweenness, and closeness. Nodal degree is positively correlated with strength, betweenness, and closeness. Unlike the common feature of a scale-free network, the most connected nodes in CRN are not necessarily the most central due to underlying geographical, political, and socioeconomic factors. We proposed an integrated measure based on the four centrality measures to identify the global role of each node and the multilayer structure of CRN and confirm that stable connections hold between different layers of CRN.

scholarly journals Organizational and Formational Structures of Networks in the Mental Lexicon: A State-Of-The-Art through Systematic Review

Languages ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Luke McCarthy ◽  
Imma Miralpeix
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Mental Lexicon ◽  
Small World ◽  
Structural Features ◽  
Future Research ◽  
Emergent Properties ◽  
Scale Free ◽  
Scale Free Networks ◽  
Network Patterns

This state-of-the-art presents a systematic exploration on the use of network patterns in global research efforts to understand, organize and represent the mental lexicon. Results have shown an increase over recent years in the usage of complex, small-world and scale-free network patterns within the literature. With the increasing complexity of network patterns, we see more potential in the inter-disciplinary exploration of the mental lexicon through universal and mathematically-describable, behavioral patterns in small-world and scale-free networks. A systematic review of 36 items of methodologically-selected literature serve as a means to explore how the greater literary body understands network structures within the mental lexicon. Network-based approaches are discriminated between three contrasting varieties. These include: ‘simple networks’, characterized by arbitrarily organized graph patterns of metaphorical importance; ‘connectionist networks’, a broad category of networks which explore the structural features of a system through the analysis of emergent properties; and lastly ‘complex networks’, distinguished as small-world, scale-free networks which follow a strict and mathematically-describable structure in agreement with the Barabási–Albert model. Each network approach is explored in terms of their discernible differences which relate to their parameters and affect their implications. A final evaluation of observed patterns within the selected literature is offered, as well as an elaboration on the sense of trajectory beheld in the research in order to offer insight and orientation for future research.

scholarly journals An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yongliang Deng ◽  
Liangliang Song ◽  
Zhipeng Zhou ◽  
Ping Liu
Keyword(s):  
Coal Mine ◽  
Path Length ◽  
Risk Control ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Mine Safety ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Scale Free

Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.

scholarly journals Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

2022 ◽  
Vol 19 (2) ◽  
pp. 689
Author(s):  
Zhangbo Yang ◽  
Jiahao Zhang ◽  
Shanxing Gao ◽  
Hui Wang
Keyword(s):  
Interpersonal Relationships ◽  
Text Analysis ◽  
Degree Distribution ◽  
Structural Characteristics ◽  
Strong Relationship ◽  
Dynamic Contact ◽  
Contact Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Free Network

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

scholarly journals Structural Properties of the Brazilian Air Transportation Network

2015 ◽  
Vol 87 (3) ◽  
pp. 1653-1674 ◽  
Author(s):  
GUILHERME S. COUTO ◽  
ANA PAULA COUTO DA SILVA ◽  
LINNYER B. RUIZ ◽  
FABRÍCIO BENEVENUTO
Keyword(s):  
Structural Properties ◽  
Complex Network ◽  
Power Law ◽  
Global Economy ◽  
Transportation Network ◽  
Small World ◽  
Air Transportation ◽  
Power Law Distribution ◽  
National Network ◽  
Operational Problem

The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City) is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

scholarly journals Simulating Market Dynamics: Interactions between Consumer Psychology and Social Networks

2003 ◽  
Vol 9 (4) ◽  
pp. 343-356 ◽  
Author(s):  
Marco A. Janssen ◽  
Wander Jager
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Small World ◽  
Systematic Investigation ◽  
Previous Article ◽  
Market Dynamics ◽  
Power Law Distribution ◽  
Scale Free ◽  
Behavioral Rules ◽  
Node Connectivity

Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

scholarly journals Co-Occurrence Network of High-Frequency Words in the Bioinformatics Literature: Structural Characteristics and Evolution

2018 ◽  
Vol 8 (10) ◽  
pp. 1994 ◽  
Author(s):  
Taoying Li ◽  
Jie Bai ◽  
Xue Yang ◽  
Qianyu Liu ◽  
Yan Chen
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Small World ◽  
Small World Network ◽  
Frequency Word ◽  
Scale Free ◽  
Rich Club ◽  
Word Selection ◽  
High Frequency Words

The subjects of literature are the direct expression of the author’s research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics literature and its structural characteristics and evolution were analysed in this paper. First, 242,891 articles from 47 top bioinformatics periodicals were chosen as the object of the study. Second, the co-occurrence relationship among high-frequency words of these articles was analysed by word segmentation and high-frequency word selection. Then, a co-occurrence network of high-frequency words in bioinformatics literature was built. Finally, the conclusions were drawn by analysing its structural characteristics and evolution. The results showed that the co-occurrence network of high-frequency words in the bioinformatics literature was a small-world network with scale-free distribution, rich-club phenomenon and disassortative matching characteristics. At the same time, the high-frequency words used by authors changed little in 2–3 years but varied greatly in four years because of the influence of the state-of-the-art technology.

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