scholarly journals Network growth models: A behavioural basis for attachment proportional to fitness

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael Bell ◽  
Supun Perera ◽  
Mahendrarajah Piraveenan ◽  
Michiel Bliemer ◽  
Tanya Latty ◽  
...  
Keyword(s):  
Growth Models ◽  
Network Growth ◽  
Network Growth Models

Visibility of Nodes in Network Growth Models

2017 ◽  
pp. 35-45 ◽  
Author(s):  
Siddharth Pal ◽  
Soham De ◽  
Tanmoy Chakraborty ◽  
Ralucca Gera
Keyword(s):  
Growth Models ◽  
Network Growth ◽  
Network Growth Models

scholarly journals Dynamics of node influence in network growth models

2021 ◽  
pp. 126520
Author(s):  
Shravika Mittal ◽  
Tanmoy Chakraborty ◽  
Siddharth Pal
Keyword(s):  
Growth Models ◽  
Network Growth ◽  
Network Growth Models

Some New Applications of Network Growth Models

2009 ◽  
pp. 217-236
Author(s):  
Gourab Ghoshal
Keyword(s):  
Growth Models ◽  
Network Growth ◽  
New Applications ◽  
Network Growth Models

scholarly journals Network growth models and genetic regulatory networks

2006 ◽  
Vol 73 (3) ◽  
Author(s):  
D. V. Foster ◽  
S. A. Kauffman ◽  
J. E. S. Socolar
Keyword(s):  
Regulatory Networks ◽  
Growth Models ◽  
Genetic Regulatory Networks ◽  
Network Growth ◽  
Network Growth Models

scholarly journals Centrality Fingerprints for Power Grid Network Growth Models

2015 ◽  
Vol 68 ◽  
pp. 52-55 ◽  
Author(s):  
Aleks Jacob Gurfinkel ◽  
Daniel A. Silva ◽  
Per Arne Rikvold
Keyword(s):  
Growth Models ◽  
Power Grid ◽  
Network Growth ◽  
Grid Network ◽  
Network Growth Models

scholarly journals Adaptive importance sampling for network growth models

2010 ◽  
Vol 189 (1) ◽  
pp. 187-203 ◽  
Author(s):  
Adam N. Guetz ◽  
Susan P. Holmes
Keyword(s):  
Importance Sampling ◽  
Growth Models ◽  
Network Growth ◽  
Adaptive Importance Sampling ◽  
Network Growth Models

scholarly journals Parallel dynamics and computational complexity of network growth models

2005 ◽  
Vol 71 (2) ◽  
Author(s):  
Benjamin Machta ◽  
Jonathan Machta
Keyword(s):  
Computational Complexity ◽  
Growth Models ◽  
Network Growth ◽  
Parallel Dynamics ◽  
Network Growth Models

scholarly journals Network Growth Modeling to Capture Individual Lexical Learning

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Nicole M. Beckage ◽  
Eliana Colunga
Keyword(s):  
Predictive Accuracy ◽  
Growth Models ◽  
Network Models ◽  
Network Growth ◽  
Phonological Representations ◽  
New Words ◽  
Underlying Network ◽  
Emergent Structure ◽  
Emergence Of Complexity ◽  
Network Growth Models

Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words play a larger role in predicting network growth as these young learners add new words to their lexicon. We also examine how the importance of these semantic and phonological representations changes during the course of development. We propose a novel and significant theoretical framework for network growth models of acquisition and test the ability of these models to predict what words a specific child is likely to learn approximately one month in the future. We find that which acquisition model best fits is influenced by the underlying network representation, the assumed process of growth, and the network centrality measure used to relate the cognitive underpinnings of acquisition to network growth. The joint importance of representation, process, and the contribution of individual words to the predictive accuracy of the network model highlights the complex and multifaceted nature of early acquisition, provides new tools, and suggests experimental hypotheses for studying lexical acquisition.

scholarly journals A mechanism for evolution of the physical concepts network

2021 ◽  
Vol 24 (2) ◽  
pp. 24001
Author(s):  
V. Palchykov ◽  
M. Krasnytska ◽  
O. Mryglod ◽  
Yu. Holovatch
Keyword(s):  
Synergistic Effect ◽  
Complex Network ◽  
Empirical Analysis ◽  
Growth Models ◽  
Underlying Mechanism ◽  
Scientific Concepts ◽  
Network Growth ◽  
Preferential Selection ◽  
Network Characteristics ◽  
Two Factors

We suggest an underlying mechanism that governs the growth of a network of concepts, a complex network that reflects the connections between different scientific concepts based on their co-occurrences in publications. To this end, we perform empirical analysis of a network of concepts based on the preprints in physics submitted to the arXiv.org. We calculate the network characteristics and show that they cannot follow as a result of several simple commonly used network growth models. In turn, we suggest that a simultaneous account of two factors, i.e., growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network. Moreover, the observed structure emerges as a synergistic effect of these both factors: each of them alone does not lead to a satisfactory picture.

scholarly journals Applications of Network Science to Education Research: Quantifying Knowledge and the Development of Expertise through Network Analysis

2020 ◽  
Vol 10 (4) ◽  
pp. 101 ◽  
Author(s):  
Cynthia S. Q. Siew
Keyword(s):  
Conceptual Change ◽  
Network Science ◽  
Growth Models ◽  
Developmental Trajectories ◽  
Knowledge Network ◽  
Network Growth ◽  
Pedagogical Strategies ◽  
Fundamental Goal ◽  
The Subject

A fundamental goal of education is to inspire and instill deep, meaningful, and long-lasting conceptual change within the knowledge landscapes of students. This commentary posits that the tools of network science could be useful in helping educators achieve this goal in two ways. First, methods from cognitive psychology and network science could be helpful in quantifying and analyzing the structure of students’ knowledge of a given discipline as a knowledge network of interconnected concepts. Second, network science methods could be relevant for investigating the developmental trajectories of knowledge structures by quantifying structural change in knowledge networks, and potentially inform instructional design in order to optimize the acquisition of meaningful knowledge as the student progresses from being a novice to an expert in the subject. This commentary provides a brief introduction to common network science measures and suggests how they might be relevant for shedding light on the cognitive processes that underlie learning and retrieval, and discusses ways in which generative network growth models could inform pedagogical strategies to enable meaningful long-term conceptual change and knowledge development among students.

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