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).

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.

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.

A Social Network Structure Analysis of Former Smokers in Emerging Markets Using China’s Twitter

2021 ◽  
Vol 7 (5) ◽  
pp. 2286-2297
Author(s):  
Tao Xiaobo ◽  
Jin Ziniu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Emerging Markets ◽  
Tobacco Control ◽  
Simulation Analysis ◽  
Small World ◽  
Scale Free ◽  
Social Network Structure ◽  
Agent Simulation ◽  
Former Smokers

Objectives: Social networks are widely used for proping the process of tobacco control in emerging markets, but their formation and effects are not well understood. Using the micro blogging platform Sina (Sina Weibo, China’s Twitter) as an example, this article conducts a multi-agent simulation analysis of the Netlogo platform to analyze the micro-level behavioral characteristics of former smokers and macro-evolutionary law in the formation of social networks in emerging markets. The results show that the tobacco control in use of social networks have two characteristics: limitations on the size of the network and the in-degree and out-degree of its nodes as well as heterogeneous attributes of the nodes. This kind of network is better at simulating a real social network than small-world and scale-free networks.

scholarly journals ADVERTISING AND IRREVERSIBLE OPINION SPREADING IN COMPLEX SOCIAL NETWORKS

2009 ◽  
Vol 20 (06) ◽  
pp. 799-815 ◽  
Author(s):  
JULIÁN CANDIA
Keyword(s):  
Social Networks ◽  
Small World ◽  
Cost Effective ◽  
Thermal Bath ◽  
Scale Free ◽  
Effective Strategies ◽  
Scale Free Networks ◽  
Variable Duration ◽  
Link Density ◽  
Spin Flips

Irreversible opinion spreading phenomena are studied on small-world and scale-free networks by means of the magnetic Eden model, a nonequilibrium kinetic model for the growth of binary mixtures in contact with a thermal bath. In this model, the opinion of an individual is affected by those of their acquaintances, but opinion changes (analogous to spin flips in an Ising-like model) are not allowed. We focus on the influence of advertising, which is represented by external magnetic fields. The interplay and competition between temperature and fields lead to order–disorder transitions, which are found to also depend on the link density and the topology of the complex network substrate. The effects of advertising campaigns with variable duration, as well as the best cost-effective strategies to achieve consensus within different scenarios, are also discussed.

scholarly journals Dynamics of Cooperation in Minority Games in Alliance Networks

2018 ◽  
Vol 10 (12) ◽  
pp. 4746 ◽  
Author(s):  
Xin-Jie Zhang ◽  
Yong Tang ◽  
Jason Xiong ◽  
Wei-Jia Wang ◽  
Yi-Cheng Zhang
Keyword(s):  
Decision Making ◽  
Social System ◽  
Social Systems ◽  
Small World ◽  
Global Strategy ◽  
Global Score ◽  
Minority Game ◽  
Scale Free ◽  
Alliance Networks ◽  
Minority Games

Alliance networks are the underlying structures of social systems in business, management, and society. The sustainability and dynamics of a social system rely on the structural evolutions of the topologies. Understanding the evolution sheds light on the dynamics and sustainability of a social system. Minority game models have been successfully applied across social science, economy, management, and engineering. They provide simple yet applicable modeling to articulate the evolutionary cooperation dynamics of competitive players in binary decision situations. By extending the minority games played in alliance networks, the cooperation in structured systems of different network topologies is analyzed. In this model, local and global score strategies are considered with and without cooperation rewiring options. The cooperation level, the score, and the topological properties are investigated. The research uses a numerical simulation approach on random networks, scale-free networks, and small-world networks. The results suggest that the network rewiring strategy leads to higher systemic performance with a higher score and a higher level of stability in decision-making. Competitive decision-making can lead to a higher level of cooperation from a poor initial start. However, stubbornness in decision-making can lead to a poor situation when cooperation is discouraged. Players with local or global information adopt local and global score strategies. The results show that local strategies might lead to imbalance, while a global strategy might achieve a relatively stable outcome. This work contributes to bridge minority games in structured networks to study the cooperation between formation and evolution, and calls for future minority game modeling on social networks.

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.

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