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.

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 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 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 On the Distributions of Subgraph Centralities in Complex Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Faxu Li ◽  
Liang Wei ◽  
Haixing Zhao ◽  
Feng Hu
Keyword(s):  
Complex Networks ◽  
Probability Distributions ◽  
Network Connectivity ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Power Law Distribution ◽  
Scale Free ◽  
Scale Free Networks ◽  
Complex Network Models

Subgraph centrality measure characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than large ones, which makes this measure appropriate for characterizing network motifs. This measure is better in being able to discriminate the nodes of a network than alternate measures. In this paper, the important issue of subgraph centrality distributions is investigated through theory-guided extensive numerical simulations, for three typical complex network models, namely, the ER random-graph networks, WS small-world networks, and BA scale-free networks. It is found that these three very different types of complex networks share some common features, particularly that the subgraph centrality distributions in increasing order are all insensitive to the network connectivity characteristics, and also found that the probability distributions of subgraph centrality of the ER and of the WS models both follow the gamma distribution, and the BA scale-free networks exhibit a power-law distribution with an exponential cutoff.

scholarly journals SPECTRAL METHODS CLUSTER WORDS OF THE SAME CLASS IN A SYNTACTIC DEPENDENCY NETWORK

2007 ◽  
Vol 17 (07) ◽  
pp. 2453-2463 ◽  
Author(s):  
RAMON FERRER I CANCHO ◽  
ANDREA CAPOCCI ◽  
GUIDO CALDARELLI
Keyword(s):  
Community Structure ◽  
Spectral Methods ◽  
Null Hypothesis ◽  
Cluster Size ◽  
Small World ◽  
Power Law Distribution ◽  
Long Range Correlations ◽  
Scale Free ◽  
Dependency Network ◽  
Syntactic Dependency

We analyze here a particular kind of linguistic network where vertices represent words and edges stand for syntactic relationships between words. The statistical properties of these networks have been recently studied and various features such as the small-world phenomenon and a scale-free distribution of degrees have been found. Our work focuses on four classes of words: verbs, nouns, adverbs and adjectives. Here, we use spectral methods sorting vertices. We show that the ordering clusters words of the same class. For nouns and verbs, the cluster size distribution clearly follows a power-law distribution that cannot be explained by a null hypothesis. Long-range correlations are found between vertices in the ordering provided by the spectral method. The findings support the use of spectral methods for detecting community structure.

scholarly journals Using Network Theory to Explore BIM Application Barriers for BIM Sustainable Development in China

2020 ◽  
Vol 12 (8) ◽  
pp. 3190
Author(s):  
Yongliang Deng ◽  
Jinyun Li ◽  
Qiuting Wu ◽  
Shuangshuang Pei ◽  
Na Xu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Network Theory ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Information Modeling ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Scale Free

Building Information Modeling (BIM) technology has promoted the development of the architecture, engineering, and construction (AEC) industry, but has encountered many barriers to its application in China. Therefore, identifying the barriers to BIM application and capturing their interactions are essential in order to control and eliminate the determined barriers. From this standpoint, 23 BIM application barriers were identified through a literature review and expert interviews. Furthermore, the interactions among them were determined based on the Delphi method, which was the foundation for establishing the BIM application barrier network (BABN). Then, the software Pajek was employed to construct the network model and reveal its topological characteristics based on complex network theory, including degree, betweenness, eigenvector, clustering coefficient, network diameter, and average path length. As indicated by the results, BABN possesses scale-free network property because its cumulative degree distribution obeys power–law distribution. BABN is also a small-world network, due to its relatively high clustering coefficient as well as small average path length, implying that barrier propagation in BABN is fast. In addition, the results are discussed and recommendations are proposed. This research will help BIM stakeholders to develop coping strategies to control and eliminate BIM application barriers for the sake of driving BIM sustainable development.

scholarly journals Co-Movement in Stock Markets Based on Directed Complex Network

2016 ◽  
Vol 8 (2) ◽  
pp. 179
Author(s):  
Zhen Du ◽  
Pujiang Chen ◽  
Na Luo ◽  
Yingjie Tang
Keyword(s):  
Complex Network ◽  
Power Law ◽  
Degree Distribution ◽  
Path Length ◽  
Stock Exchange ◽  
Small World ◽  
Clustering Coefficient ◽  
Power Law Distribution ◽  
Scale Free ◽  
Set Up

<p>In this paper, directed complex network is applied to the study of A shares in SSE (Shanghai Stock Exchange). In order to discuss the intrinsic attributes and regularities in stock market, we set up a directed complex network, selecting 450 stocks as nodes between 2012 and 2014 and stock yield correlation connected as edges. By discussing out-degree and in-degree distribution, we find essential nodes in stock network, which represent the leading stock,. Moreover, we analyze directed average path length and clustering coefficient in the condition of different threshold, which shows that the network doesn’t have a small- world effect. Furthermore, we see that when threshold is between 0.08 and 0.15, the network follows the power-law distribution and behaves scale-free.</p>

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.

scholarly journals Identifying Vulnerable Nodes of Complex Networks in Cascading Failures Induced by Node-Based Attacks

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Shudong Li ◽  
Lixiang Li ◽  
Yan Jia ◽  
Xinran Liu ◽  
Yixian Yang
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Small World ◽  
Cascading Failures ◽  
Small World Networks ◽  
Power Law Distribution ◽  
Scale Free ◽  
Potential Safety ◽  
High Degree ◽  
Fourth Kind

In the research on network security, distinguishing the vulnerable components of networks is very important for protecting infrastructures systems. Here, we probe how to identify the vulnerable nodes of complex networks in cascading failures, which was ignored before. Concerned with random attack (RA) and highest load attack (HL) on nodes, we model cascading dynamics of complex networks. Then, we introduce four kinds of weighting methods to characterize the nodes of networks including Barabási-Albert scale-free networks (SF), Watts-Strogatz small-world networks (WS), Erdos-Renyi random networks (ER), and two real-world networks. The simulations show that, for SF networks under HL attack, the nodes with small value of the fourth kind of weight are the most vulnerable and the ones with small value of the third weight are also vulnerable. Also, the real-world autonomous system with power-law distribution verifies these findings. Moreover, for WS and ER networks under both RA and HL attack, when the nodes have low tolerant ability, the ones with small value of the fourth kind of weight are more vulnerable and also the ones with high degree are easier to break down. The results give us important theoretical basis for digging the potential safety loophole and making protection strategy.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

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