scholarly journals K-clique percolation in free association networks. The mechanism behind the 7 ± 2 law?

2021 ◽  
Author(s):  
Olga Valba ◽  
Alexander Gorsky
Keyword(s):  
Community Organization ◽  
Preferential Attachment ◽  
Free Association ◽  
Small World ◽  
Semantic Networks ◽  
Network Evolution ◽  
Cognitive Networks ◽  
Qualitative Explanation ◽  
Capacity Limit ◽  
Clique Percolation

Abstract It is important to reveal the mechanisms of propagation in different cognitive networks. In this study we discuss the k-clique percolation phenomenon on the free association networks including "English Small World of Words project" (SWOW-EN). We compare different semantic networks and networks of free associations for different languages. Surprisingly it turned out that k-clique percolation for all k < k c = (6 − 7) is possible on free association networks of different languages. Our analysis suggests the new universality patterns for a community organization of free association networks. We conjecture that our result can provide the qualitative explanation of the Miller’s 7 ± 2 rule for the capacity limit of working memory. The new model of network evolution extending the preferential attachment is suggested which provides the observed value of k c .

Epidemic spreading on evolving networks

2019 ◽  
Vol 33 (23) ◽  
pp. 1950266 ◽  
Author(s):  
Jin-Xuan Yang
Keyword(s):  
Preferential Attachment ◽  
Small World ◽  
Network Evolution ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Scale Free ◽  
Evolving Networks ◽  
Power Law Exponent ◽  
Free Network ◽  
Over Time

Network structure will evolve over time, which will lead to changes in the spread of the epidemic. In this work, a network evolution model based on the principle of preferential attachment is proposed. The network will evolve into a scale-free network with a power-law exponent between 2 and 3 by our model, where the exponent is determined by the evolution parameters. We analyze the epidemic spreading process as the network evolves from a small-world one to a scale-free one, including the changes in epidemic threshold over time. The condition of epidemic threshold to increase is given with the evolution processes. The simulated results of real-world networks and synthetic networks show that as the network evolves at a low evolution rate, it is more conducive to preventing epidemic spreading.

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.

Estimating Local Decision-Making Behavior in Complex Evolutionary Systems

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Zhenghui Sha ◽  
Jitesh H. Panchal
Keyword(s):  
Decision Making ◽  
Systems Engineering ◽  
Choice Model ◽  
Preferential Attachment ◽  
Network Evolution ◽  
System Structure ◽  
Statistical Regression ◽  
Evolutionary Systems ◽  
Local Decision ◽  
Local Decision Making

Research in systems engineering and design is increasingly focused on complex sociotechnical systems whose structures are not directly controlled by the designers, but evolve endogenously as a result of decisions and behaviors of self-directed entities. Examples of such systems include smart electric grids, Internet, smart transportation networks, and open source product development communities. To influence the structure and performance of such systems, it is crucial to understand the local decisions that result in observed system structures. This paper presents three approaches to estimate the local behaviors and preferences in complex evolutionary systems, modeled as networks, from its structure at different time steps. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the local decision-making behaviors from consecutive snapshots of the system structure. In the third approach, the entities are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-centric framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The approaches are illustrated and compared using an example of the autonomous system (AS) level Internet. The approaches are generally applicable to a variety of complex systems that can be modeled as networks. The insights gained are expected to direct researchers in choosing the most applicable estimation approach to get the node-level behaviors in the context of different scenarios.

scholarly journals A Novel Wireless Sensor Network Evolution Model Based on Energy-Efficiency

2017 ◽  
Vol 13 (03) ◽  
pp. 4 ◽  
Author(s):  
Hui Gao ◽  
Zhixian Yang
Keyword(s):  
Preferential Attachment ◽  
Influence Factors ◽  
Network Evolution ◽  
Evolution Model ◽  
Wireless Sensor ◽  
Font Size ◽  
Total Energy Consumption ◽  
Scale Free ◽  
Random Node ◽  
Attachment Mechanism

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">The Barabási–Albert (BA) model is a famous complex network model that generates scale-free networks. Wireless sensor networks (WSNs) had been thought to be approximately scale-free through lots of empirical research. Based on the BA model, we propose an evolution model for WSNs. According to actual influence factors such as the remainder energy of each sensor and physical link capability of each sensor, our evolution model constructs WSNs by using a preferential attachment mechanism. Through simulation and analysis, we can prove that our evolution model would make the total energy consumption of the WSNs more efficient and have a superior random node error tolerance.</span>

The N-star network evolution model

2019 ◽  
Vol 56 (2) ◽  
pp. 416-440 ◽  
Author(s):  
István Fazekas ◽  
Csaba Noszály ◽  
Attila Perecsényi
Keyword(s):  
Power Law ◽  
Preferential Attachment ◽  
Network Evolution ◽  
Evolution Model ◽  
Asymptotic Power ◽  
Star Network ◽  
Power Law Distributions

AbstractA new network evolution model is introduced in this paper. The model is based on cooperations of N units. The units are the nodes of the network and the cooperations are indicated by directed links. At each evolution step N units cooperate, which formally means that they form a directed N-star subgraph. At each step either a new unit joins the network and it cooperates with N − 1 old units, or N old units cooperate. During the evolution both preferential attachment and uniform choice are applied. Asymptotic power law distributions are obtained both for in-degrees and for out-degrees.

scholarly journals A quality-of-service-aware dynamic evolution model for space–ground integrated network

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772864 ◽  
Author(s):  
Zhuo Yi ◽  
Xuehui Du ◽  
Ying Liao ◽  
Lifeng Cao
Keyword(s):  
Quality Of Service ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Evolution Model ◽  
Dynamic Evolution ◽  
Power Law Distribution ◽  
Integrated Network ◽  
Complex Network Theory

Space–ground integrated network, a strategic, driving, and irreplaceable infrastructure, guarantees the development of economic and national security. However, its natures of limited resources, frequent handovers, and intermittently connected links significantly reduce the quality of service. To address this issue, a quality-of-service-aware dynamic evolution model is proposed based on complex network theory. On one hand, a quality-of-service-aware strategy is adopted in the model. During evolution phases of growth and handovers, links are established or deleted according to the quality-of-service-aware preferential attachment following the rule of better quality of service getting richer and worse quality of service getting poor or to die. On the other hand, dynamic handover of nodes and intermittent connection of links are taken into account and introduced into the model. Meanwhile, node heterogeneity is analyzed and heterogeneous nodes are endowed with discriminate interactions. Theoretical analysis and simulations are utilized to explore the degree distribution and its characteristics. Results reveal that this model is a scale-free model with drift power-law distribution, fat-tail and small-world effect, and drift character of degree distribution results from dynamic handover. Furthermore, this model exerts well fault tolerance and attack resistance compared to signal-strength-based strategy. In addition, node heterogeneity and quality-of-service-aware strategy improve the attack resistance and overall quality of service of space–ground integrated network.

On the Topology Design of Integrated Wireless Networks

2011 ◽  
Vol 55-57 ◽  
pp. 555-560
Author(s):  
Xin Yan ◽  
Jia Gen Du
Keyword(s):  
Wireless Networks ◽  
Communication Networks ◽  
Topology Control ◽  
Preferential Attachment ◽  
Small World ◽  
Topology Design ◽  
Relay Nodes ◽  
Time Step ◽  
Scale Free ◽  
Proximity Graph

Topology clustering, constructing overlay graphs, adding relay nodes, or creating a “small-world” network by building some shortcuts etc, topology control schemes are able to achieve the scalability, resilience, and fault-tolerance for wireless communication networks. In this article, we take a different approach to reach such aim for heterogeneous integrated wireless networks by generating a topology such that the resulting network is “scale-free”. Thereby we propose a topology control algorithm based on the “scale-free” complex network concept and directed proximity graph theory for integrated wireless networks with non-uniform transmission ranges. In this algorithm, the topology is also generated by new nodes’ growth and preferential attachment procedure, but where each new node connects to the existing nodes in its directed attachable proximity in terms of certain probability at each time step. Each node’s directed attachable proximity graph is generated from its directed reachable proximity graph that is built by regulating its transmission power based on locally collected information. The simulation experiments are provided to validate our claims.

scholarly journals Exploring the topology and dynamic growth properties of co-invention networks and technology fields

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256956
Author(s):  
Pablo E. Pinto ◽  
Guillermo Honores ◽  
Andrés Vallone
Keyword(s):  
Preferential Attachment ◽  
Small World ◽  
Individual Characteristics ◽  
Collaboration Networks ◽  
Technological Diversification ◽  
Sudden Rise ◽  
Growth Properties ◽  
Dynamic Growth ◽  
The Individual ◽  
Inventor Networks

This study investigates the topology and dynamics of collaboration networks that exist between inventors and their patent co-authors for patents granted by the USPTO from 2007–2019 (2,241,201 patents and 1,879,037 inventors). We study changes in the configurations of different technology fields via the power-law, small-world, preferential attachment, shrinking diameter, densification law, and gelling point hypotheses. Similar to the existing literature, we obtain mixed results. Based on network statistics, we argue that the sudden rise of large networks in six technology sectors can be understood as a phase transition in which small, isolated networks form one giant component. In two other technology sectors, such a transition occurred much later and much less dramatically. The examination of inventor networks over time reveals the increased complexity of all technology sectors, regardless of the individual characteristics of the network. Therefore, we introduce ideas associated with the technological diversification of inventors to complement our analysis, and we find evidence that inventors tend to diversify into new fields that are less mature. This behavior appears to be correlated with the compliance of some of the expected network rules and has implications for the emerging patterns among the different collaboration networks under consideration here.

scholarly journals A meritocratic network formation model for the rise of social media influencers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicolò Pagan ◽  
Wenjun Mei ◽  
Cheng Li ◽  
Florian Dörfler
Keyword(s):  
Online Social Networks ◽  
Network Formation ◽  
Preferential Attachment ◽  
Scaling Law ◽  
Small World ◽  
Large Data ◽  
Large Data Sets ◽  
Directed Network ◽  
Data Sets ◽  
Quality Ranking

AbstractMany of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.

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