scholarly journals An investigation of network growth principles in the phonological language network

2020 ◽  
Author(s):  
Cynthia S. Q. Siew ◽  
Michael Vitevitch
Keyword(s):  
Learning Outcomes ◽  
Preferential Attachment ◽  
Language Disorders ◽  
Age Of Acquisition ◽  
Network Growth ◽  
Phonological Representations ◽  
New Words ◽  
Lexical Representations ◽  
Over Time ◽  
Language Network

This paper investigated how network growth algorithms—preferential attachment, preferential acquisition, and lure of the associates—relate to the acquisition of words in the phonological language network, where edges are placed between words that are phonologically similar to each other. Through an archival analysis of age-of-acquisition norms from English and Dutch and word learning experiments, we examined how new words were added to the phonological network. Across both approaches, we found converging evidence that an inverse variant of preferential attachment—where new nodes were instead more likely to attach to existing nodes with few connections—influenced the growth of the phonological network. We suggest that the inverse preferential attachment principle reflects the constraints of adding new phonological representations to an existing language network with already many phonologically similar representations, possibly reflecting the pressures associated with the processing costs of retrieving lexical representations that have many phonologically similar competitors. These results contribute toward our understanding of how the phonological language network grows over time and could have implications for the learning outcomes of individuals with language disorders.

scholarly journals Likelihood-based approach to discriminate mixtures of network models that vary in time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi A. Arnold ◽  
Raul J. Mondragón ◽  
Richard G. Clegg
Keyword(s):  
Network Model ◽  
Change Point ◽  
Network Science ◽  
Preferential Attachment ◽  
Network Models ◽  
Explanatory Models ◽  
Growth Mechanisms ◽  
Artificial Data ◽  
Network Growth ◽  
Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

scholarly journals Investigating the Influence of Inverse Preferential Attachment on Network Development

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1029
Author(s):  
Cynthia S. Q. Siew ◽  
Michael S. Vitevitch
Keyword(s):  
Growth Mechanism ◽  
Simulation Study ◽  
Preferential Attachment ◽  
Phonological Similarity ◽  
Network Development ◽  
Growth Mechanisms ◽  
Network Growth ◽  
New Words ◽  
Path Lengths ◽  
Power Law Distributions

Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure.

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 Friendship paradox in growth networks: analytical and empirical analysis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sergei P. Sidorov ◽  
Sergei V. Mironov ◽  
Alexey A. Grigoriev
Keyword(s):  
Real World ◽  
Preferential Attachment ◽  
Empirical Studies ◽  
Mean Field ◽  
Network Models ◽  
Average Degree ◽  
Closure Model ◽  
Triadic Closure ◽  
Attachment Mechanism ◽  
Over Time

AbstractMany empirical studies have shown that in social, citation, collaboration, and other types of networks in real world, the degree of almost every node is less than the average degree of its neighbors. This imbalance is well known in sociology as the friendship paradox and states that your friends are more popular than you on average. If we introduce a value equal to the ratio of the average degree of the neighbors for a certain node to the degree of this node (which is called the ‘friendship index’, FI), then the FI value of more than 1 for most nodes indicates the presence of the friendship paradox in the network. In this paper, we study the behavior of the FI over time for networks generated by growth network models. We will focus our analysis on two models based on the use of the preferential attachment mechanism: the Barabási–Albert model and the triadic closure model. Using the mean-field approach, we obtain differential equations describing the dynamics of changes in the FI over time, and accordingly, after obtaining their solutions, we find the expected values of this index over iterations. The results show that the values of FI are decreasing over time for all nodes in both models. However, for networks constructed in accordance with the triadic closure model, this decrease occurs at a much slower rate than for the Barabási–Albert graphs. In addition, we analyze several real-world networks and show that their FI distributions follow a power law. We show that both the Barabási–Albert and the triadic closure networks exhibit the same behavior. However, for networks based on the triadic closure model, the distributions of FI are more heavy-tailed and, in this sense, are closer to the distributions for real networks.

scholarly journals Firm’s strategic choices and network knowledge dynamics: how do they affect innovation?

2018 ◽  
Vol 22 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Jose Antonio Belso-Martinez ◽  
Isabel Diez-Vial
Keyword(s):  
Absorptive Capacity ◽  
Knowledge Networks ◽  
Knowledge Network ◽  
Strategic Choices ◽  
Network Growth ◽  
Content Type ◽  
Endogenous Factors ◽  
The Impact ◽  
Innovative Capacity ◽  
Over Time

Purpose This paper aims to explain how the evolution of knowledge networks and firms’ strategic choices affect innovation. Endogenous factors associated with a path-dependent evolution of the knowledge network are jointly considered with a firm’s development of international relationships and increasing internal absorptive capacity over time. Design/methodology/approach In a biotech cluster, the authors gathered data on the firms’ characteristics and network relationships by asking about the technological knowledge they received in the cluster in 2007 and 2012 – “roster-recall” method. Estimation results were obtained using moderated regression analysis. Findings Firms that increase their involvement in knowledge networks over time also tend to increase their innovative capacity. However, efforts devoted to building international links or absorptive capacity negatively moderate the impact of network growth on innovation. Practical implications Practitioners have two alternative ways of increasing innovation inside knowledge networks: they can increase their centrality by developing their knowledge network interactions or invest in developing their internal absorptive capacity and new international sources of knowledge. Investing in both of these simultaneously does not seem to improve a firm’s innovative capacity. Originality/value Coupling firms’ strategic options with knowledge network dynamics provide a more complete way of explaining how firms can improve their innovative capacity.

scholarly journals Ambiguity in language networks

2015 ◽  
Vol 32 (1) ◽  
Author(s):  
Ricard V. Solé ◽  
Luís F. Seoane
Keyword(s):  
Language Processing ◽  
Network Architecture ◽  
Essential Role ◽  
Scaling Laws ◽  
Network Growth ◽  
Computational Approaches ◽  
Universal Scaling ◽  
Different Levels ◽  
Relevant Component ◽  
Language Network

AbstractHuman language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among others, semantics. All linguistic levels have to deal with an astronomic combinatorial potential that stems from the recursive nature of languages. This recursiveness is indeed a key defining trait. However, not all words are equally combined nor frequent. In breaking the symmetry between less and more often used and between less and more meaning-bearing units, universal scaling laws arise. Such laws, common to all human languages, appear on different stages from word inventories to networks of interacting words. Among these seemingly universal traits exhibited by language networks, ambiguity appears to be a specially relevant component. Ambiguity is avoided in most computational approaches to language processing, and yet it seems to be a crucial element of language architecture. Here we review the evidence both from language network architecture and from theoretical reasonings based on a least effort argument. Ambiguity is shown to play an essential role in providing a source of language efficiency, and is likely to be an inevitable byproduct of network growth.

scholarly journals Mapping Internationalization on US Campuses

2018 ◽  
pp. 11-13 ◽  
Author(s):  
Lucia Brajkovic ◽  
Robin Matross Helms
Keyword(s):  
Learning Outcomes ◽  
Student Mobility ◽  
Colleges And Universities ◽  
American Council ◽  
Current State ◽  
Trends Over Time ◽  
Administrative Structures ◽  
Comprehensive Internationalization ◽  
Over Time ◽  
Policies And Practices

This article summarizes the results of the American Council on Education’s (ACE) 2016 Mapping Internationalization on U.S. Campuses survey, and explores their implications. Conducted every five years, Mapping assesses the current state of internationalization at American colleges and universities, analyzes progress and trends over time, and identifies future priorities. The 2016 Mapping survey addressed the six key areas that comprise CIGE’s Model for Comprehensive Internationalization: articulated commitment; administrative structures and staffing; curriculum, cocurriculum, and learning outcomes; faculty policies and practices; student mobility; and collaboration and partnerships.

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.

The evolution of collaborative networks: A social network analysis of Chinese environmental protection policy

2021 ◽  
pp. 095207672110346
Author(s):  
Yanwei Li ◽  
Jing Huang
Keyword(s):  
Environmental Protection ◽  
Preferential Attachment ◽  
Building Blocks ◽  
National Government ◽  
Collaborative Networks ◽  
Government Agencies ◽  
Collaborative Network ◽  
Network Partner ◽  
Societal Problems ◽  
Over Time

Interagency collaboration helps governments to better resolve various complex societal problems. This contribution examines the mechanisms underlying the collaboration of disparate national government agencies engaged in Chinese environmental protection. We test three dominant mechanisms, namely, the institutionalization of collaborative networks, resource interdependence and exchange, and preferential attachment. It is concluded that a collaborative network over time becomes cohesive, that national government agencies prefer to collaborate with popular agencies and tend to collaborate with those whose resources are different from their own, and that popular agencies tend to maintain their core positions over time. Our study enriches the current governance and policy literature through adding building blocks for the evolution of collaborative network and network partner selection.

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