Words, constructions and corpora: Network representations of constructional semantics for Mandarin space particles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alvin Cheng-Hsien Chen
Keyword(s):  
Clustering Coefficient ◽  
Linguistic Knowledge ◽  
Scale Free ◽  
Network Analyses ◽  
Semantic Fields ◽  
Text Corpora ◽  
Quantitative Properties ◽  
Local Clustering Coefficient ◽  
Linguistic Units

AbstractIn this study, we aim to demonstrate the effectiveness of network science in exploring the emergence of constructional semantics from the connectedness and relationships between linguistic units. With Mandarin locative constructions (MLCs) as a case study, we extracted constructional tokens from a representative corpus, including their respective space particles (SPs) and the head nouns of the landmarks (LMs), which constitute the nodes of the network. We computed edges based on the lexical similarities of word embeddings learned from large text corpora and the SP-LM contingency from collostructional analysis. We address three issues: (1) For each LM, how prototypical is it of the meaning of the SP? (2) For each SP, how semantically cohesive are its LM exemplars? (3) What are the emerging semantic fields from the constructional network of MLCs? We address these questions by examining the quantitative properties of the network at three levels: microscopic (i.e., node centrality and local clustering coefficient), mesoscopic (i.e., community) and macroscopic properties (i.e., small-worldness and scale-free). Our network analyses bring to the foreground the importance of repeated language experiences in the shaping and entrenchment of linguistic knowledge.

scholarly journals EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity—A Machine Learning Approach

2020 ◽  
Vol 9 (12) ◽  
pp. 3934
Author(s):  
Jeong-Youn Kim ◽  
Hyun Seo Lee ◽  
Seung-Hwan Lee
Keyword(s):  
Resting State ◽  
Symptom Severity ◽  
Classification Accuracy ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Forward Selection ◽  
Linear Discriminant ◽  
Network Analyses ◽  
Local Clustering Coefficient

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

scholarly journals Disparity of clustering coefficients in the Holme‒Kim network model

2018 ◽  
Vol 50 (3) ◽  
pp. 918-943
Author(s):  
R. I. Oliveira ◽  
R. Ribeiro ◽  
R. Sanchis
Keyword(s):  
Random Graph ◽  
Network Model ◽  
Slow Rate ◽  
Clustering Coefficient ◽  
Concentration Inequality ◽  
Free Graph ◽  
Scale Free ◽  
Local Clustering ◽  
Clustering Coefficients ◽  
Local Clustering Coefficient

Abstract The Holme‒Kim random graph process is a variant of the Barabási‒Álbert scale-free graph that was designed to exhibit clustering. In this paper we show that whether the model does indeed exhibit clustering depends on how we define the clustering coefficient. In fact, we find that the local clustering coefficient typically remains positive whereas global clustering tends to 0 at a slow rate. These and other results are proven via martingale techniques, such as Freedman's concentration inequality combined with a bootstrapping argument.

scholarly journals Analysis and synthesis of a growing network model generating dense scale-free networks via category theory

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Taichi Haruna ◽  
Yukio-Pegio Gunji
Keyword(s):  
Network Model ◽  
Category Theory ◽  
Clustering Coefficient ◽  
Scale Free ◽  
Degree Correlation ◽  
Proposed Model ◽  
Scale Free Networks ◽  
Analysis And Synthesis ◽  
Local Clustering Coefficient ◽  
Growing Network

AbstractWe propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure.

Mapping OWL-S Process Model to Timed Automata

2018 ◽  
Vol 11 (1) ◽  
pp. 29-48
Author(s):  
Amel Boumaza ◽  
Ramdane Maamri
Keyword(s):  
Web Services ◽  
Process Model ◽  
Timed Automata ◽  
Automatic Verification ◽  
Covering Problems ◽  
Field Verification ◽  
Starting Point ◽  
Quantitative Properties ◽  
Time Systems

The conversion of web services to semantic web comes the opportunity to automate various tasks. OWL-S plays a key role in describing web services behaviour. While ontology-based semantics given to OWL-S is structural rather than behaviourally oriented, we cannot automate an essential task in this field, verification. In this article, the mapping of OWL-S process model to Timed automata is investigated, which is a suitable formalism for real time systems modeling and automatic verification. Hence, this has led to not only enabling automatic verification but also covering problems related to automated verification of temporal quantitative properties as bounded liveness property. As a starting point, the OWL-S and sub entry of time ontologies for describing the timed behaviour of services has been chosen. A defined set of mapping rules is used to automatically encode control constructs defined in OWL-S and temporal information into timed automata. Also, it is shown how a Uppaal checker is used to check required properties formulated in TCTL. Finally, an EClinic case study is used to illustrate the technique.

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this network.

scholarly journals Quantitative method for resilience assessment framework of airport network during COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260940
Author(s):  
Jiuxia Guo ◽  
Yang Li ◽  
Zongxin Yang ◽  
Xinping Zhu
Keyword(s):  
Complex Network ◽  
Small World ◽  
Global Scale ◽  
Assessment Framework ◽  
Small World Networks ◽  
Scale Free ◽  
Global Pandemic ◽  
Network Metrics ◽  
Resilience Assessment

The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

scholarly journals Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1744
Author(s):  
Stefania Pilati ◽  
Giulia Malacarne ◽  
David Navarro-Payá ◽  
Gabriele Tomè ◽  
Laura Riscica ◽  
...  
Keyword(s):  
Gene Networks ◽  
Functional Characterization ◽  
Gene Families ◽  
Stilbene Synthase ◽  
Breeding Programs ◽  
Transcriptomic Data ◽  
Network Analyses ◽  
Manual Curation ◽  
Inference Methods

The abundance of transcriptomic data and the development of causal inference methods have paved the way for gene network analyses in grapevine. Vitis OneGenE is a transcriptomic data mining tool that finds direct correlations between genes, thus producing association networks. As a proof of concept, the stilbene synthase gene regulatory network obtained with OneGenE has been compared with published co-expression analysis and experimental data, including cistrome data for MYB stilbenoid regulators. As a case study, the two secondary metabolism pathways of stilbenoids and lignin synthesis were explored. Several isoforms of laccase, peroxidase, and dirigent protein genes, putatively involved in the final oxidative oligomerization steps, were identified as specifically belonging to either one of these pathways. Manual curation of the predicted sequences exploiting the last available genome assembly, and the integration of phylogenetic and OneGenE analyses, identified a group of laccases exclusively present in grapevine and related to stilbenoids. Here we show how network analysis by OneGenE can accelerate knowledge discovery by suggesting new candidates for functional characterization and application in breeding programs.

scholarly journals Studies on the Decrease Mechanisms of Typical Complex Networks

2021 ◽  
Author(s):  
Yuhu Qiu ◽  
Tianyang Lyu ◽  
Xizhe Zhang ◽  
Ruozhou Wang
Keyword(s):  
Average Length ◽  
Shortest Paths ◽  
Real Life ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Network Growth ◽  
Scale Free ◽  
Complex Network Models ◽  
High Degree

Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.

LESS DIVIDED AFTER ETA? THE EVOLUTION OF IDEOLOGICAL CLEAVAGES IN THE BASQUE ENVIRONMENTAL FIELD, 2007–2017

2021 ◽  
Vol 26 (2) ◽  
pp. 217-236
Author(s):  
Alejandro Ciordia
Keyword(s):  
Collective Action ◽  
Political Violence ◽  
Social Life ◽  
Strong Influence ◽  
Basque Country ◽  
Political Community ◽  
National Identities ◽  
Network Analyses ◽  
Civic Organizations

The Basque Country has traditionally been considered a strongly polarized political community. The influence of the center-periphery cleavage and the shadow of political violence have conditioned many aspects of social life, including relations among civic organizations. Previous literature suggests that differences in organizations’ national identities and/or position towards ETA’s (Euskadi ta Askatasuna, or Basque Country and Freedom in the Basque language) violence have often acted as cleavages fragmenting collective action fields. This research examines whether this picture changed substantially after ETA’s abandonment of violence in 2011 by taking the environmental field as a case study and looking at the evolution of patterns of interorganizational collaboration between 2007 and 2017. The results of statistical network analyses show that both Basque nationalism and ideological positions towards ETA’s use of violence had a strong influence on organizations’ decisions to collaborate with one another up to 2011, whereas during the more recent postconflict period, collaboration seems to occur in a more pluralistic and less ideologically driven fashion.

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