Using network science and psycholinguistic megastudies to examine the dimensions of phonological similarity

2020 ◽  
Author(s):  
Nichol Castro ◽  
Michael Vitevitch
Keyword(s):  
Network Science ◽  
Small World ◽  
Phonological Similarity ◽  
Neighborhood Density ◽  
Theoretic Approach ◽  
Scale Free ◽  
Advantages And Disadvantages ◽  
Behavioral Evidence ◽  
The One ◽  
Information Theoretic Approach

We used the tools of network science to examine different dimensions of phonological similarity. Data from a phonological associate task and from an identification of words in noise task were used to create two separate networks. The resulting networks were compared to each other and to a network formed by a computational metric (i.e., one-phoneme metric) widely-used to assess phonological similarity using an information-theoretic approach and a variety of network measures (e.g., small-world structure, scale-free structure, mixing by degree, location of nodes in the network, and community structure). While we found that a network formed by the one-phoneme metric was structurally less similar to the network formed from the phonological associate task and to the network formed from the identification of words in noise task than the latter two were to each other, there were also several common network structure features between the one-phoneme metric network and the phonological association network. We then compared the influence of degree (equivalent to neighborhood density) from each of the networks on behavioral data, namely reaction time on visual and auditory lexical decision tasks, obtained from two psycholinguistic megastudies to provide behavioral evidence for differences in network structures. We found that the effect of degree differed across network types and tasks. We discuss the advantages and disadvantages of each approach to determining phonological similarity, the implications of using each approach, and a possible direction forward for language research through the use of multiplex networks.

Evolution Model of Technology Innovation Cooperation Network of Software Enterprise

2014 ◽  
Vol 539 ◽  
pp. 355-359
Author(s):  
Wen Long Yu
Keyword(s):  
Technology Innovation ◽  
Target Selection ◽  
Small World ◽  
Selection Strategy ◽  
Complex Network Theory ◽  
Scale Free ◽  
Advantages And Disadvantages ◽  
Enterprise Technology ◽  
Cooperation Network ◽  
Innovation Cooperation

This article will complex network theory is introduced to the world's leading technology innovation cooperation network study, continue to deepen the theoretical study, by numerical simulation and empirical research on the method of combining technical innovation cooperation network to the evolution mechanism and its small world, scale-free characteristics were studied. First perfect the network modeling work decline period. Secondly, put forward the strategy of technology innovation diffusion, namely random selection, target selection and associated enterprises selection strategy, and compares the advantages and disadvantages of relationship between different strategies. Finally, combined the status quo of the cooperation technological innovation in our country, respectively from two levels to enterprises and government strengthen enterprise technical cooperation and improve enterprise technology innovation ability, and puts forward some Suggestions and countermeasures of technology innovation cooperation network further study is forecasted.

scholarly journals Robustness in Weighted Networks with Cluster Structure

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yi Zheng ◽  
Fang Liu ◽  
Yong-Wang Gong
Keyword(s):  
Betweenness Centrality ◽  
Cluster Structure ◽  
Small World ◽  
Weighting Scheme ◽  
Clustering Coefficient ◽  
Fixed Degree ◽  
Scale Free ◽  
Optimal Region ◽  
Weighting Strategy ◽  
The One

The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.

scholarly journals From a single network to a network of networks

2014 ◽  
Vol 1 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Jianxi Gao ◽  
Daqing Li ◽  
Shlomo Havlin
Keyword(s):  
Biological Networks ◽  
Network Science ◽  
Small World ◽  
Spatial Constraints ◽  
Scale Free ◽  
Gene Regulation Network ◽  
Interdependent Networks ◽  
Interconnected Networks ◽  
The Rich ◽  
Network Of Networks

Abstract Network science has attracted much attention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-profile publications, and it seems that since 2010 studies of ‘network of networks’ (NON), sometimes called multilayer networks or multiplex, have attracted more and more attention. The analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensively in physics and mathematics in the last 50 years is a specific limit of the rich and very different general case of n coupled networks. Since then, properties and dynamics of interdependent and interconnected networks have been studied extensively, and scientists are finding many interesting results and discovering many surprising phenomena. Because most natural and engineered systems are composed of multiple subsystems and layers of connectivity, it is important to consider these features in order to improve our understanding of such complex systems. Now the study of NON has become one of the important directions in network science. In this paper, we review recent studies on the new emerging area—NON. Due to the fast growth of this field, there are many definitions of different types of NON, such as interdependent networks, interconnected networks, multilayered networks, multiplex networks and many others. There exist many datasets that can be represented as NON, such as network of different transportation networks including flight networks, railway networks and road networks, network of ecological networks including species interacting networks and food webs, network of biological networks including gene regulation network, metabolic network and protein–protein interacting network, network of social networks and so on. Among them, many interdependent networks including critical infrastructures are embedded in space, introducing spatial constraints. Thus, we also review the progress on study of spatially embedded networks. As a result of spatial constraints, such interdependent networks exhibit extreme vulnerabilities compared with their non-embedded counterparts. Such studies help us to understand, realize and hopefully mitigate the increasing risk in NON.

Hybrid polymer composite materials. Part 2. Preparation technology

2020 ◽  
pp. 8-13
Keyword(s):  
Composite Materials ◽  
Impact Toughness ◽  
Polymer Composites ◽  
The Other ◽  
Polymer Materials ◽  
Hybrid Polymer ◽  
Preparation Technology ◽  
Advantages And Disadvantages ◽  
Hybrid Polymer Composite ◽  
The One

The main methods (pressing and winding) of the processing of hybrid polymer composites to obtain items were examined. Advantages and disadvantages of the methods were noted. Good combinations of different-module fibers (carbon, glass, boron, organic) in hybrid polymer materials are described, which allow one to prepare materials with high compression strength on the one hand, and to increase fracture energy of samples and impact toughness on the other hand.

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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