scholarly journals K-core analysis and modeling for network centralities

2018 ◽  
Vol 7 (2.7) ◽  
pp. 168
Author(s):  
V MNSSVKR Gupta ◽  
Ch V.Phani Krishna
Keyword(s):  
Network Modeling ◽  
Network Graph ◽  
Frequency Limit ◽  
Cloud Data ◽  
Undirected Network ◽  
Word Cloud ◽  
Interdisciplinary Study ◽  
Network Centralities ◽  
Word Frequencies ◽  
Different Levels

Network modeling is the interdisciplinary study of relationships. Network analysis deals with relational data and Network modeling represents the interdisciplinary study of relationships. Network structure can be studied at many different levels. Around 1000 article titles on cancer, published in journal resources were considered as a dataset. Data exploration was done through displaying nodes and edges in various layouts. With a term frequency limit of 100, nearly 64 terms appeared which less than 1% sparse is. Word cloud data was plotted using word frequencies from term matrix data. An undirected network graph plotted and evaluated density, average path length and modularity, which were found to be within limits. K-cores have also been used to analyze the connectivity of a network. Network centralities such as Degree centrality, Closeness, Eigenvector and between’s centrality resulted in node carcinoma being more central in the network.

Network analysis as a method of assessing terms in a dataset comprising eating disorders

2018 ◽  
Vol 7 (2.7) ◽  
pp. 841
Author(s):  
Dr Adimulam Yesu Babu ◽  
Dr Deepak Nedunuri ◽  
T Venkata Sai Krishna
Keyword(s):  
Eating Disorders ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Agglomerative Hierarchical Clustering ◽  
Network Graph ◽  
Undirected Network ◽  
Ward Clustering ◽  
R Programming ◽  
Network Centralities ◽  
Word Frequencies

Eating disorders are central reason of physical and psycho-social morbidity. Several factors have been identified as being associated with the prevalence and progression of eating disorders in humans. Scientific investigation was carried out to assess the usage of terms in manuscript titles of nearly 900 published articles followed by network analysis and network centralities using R programming. The tm package, term document matrix function was utilized to create a term document matrix (TDM) from the corpus. A binary word matrix comprising 17 terms was created based on higher probability of occurring a term in a column. An agglomerative hierarchical clustering technique using ward clustering algorithm was presented. A data frame from the TDM was created to store data and used to plot word cloud based on word frequencies. An undirected network graph was plotted based on terms that appeared in the term matrix. Centralization measures such as Degree centrality, Closeness, Eigenvector and betweenness Centrality were reported.  

Centrality Analysis of the United States Network Graph

2018 ◽  
pp. 1746-1756
Author(s):  
Natarajan Meghanathan
Keyword(s):  
United States ◽  
Betweenness Centrality ◽  
The United States ◽  
Power Law Distribution ◽  
Network Graph ◽  
Central State ◽  
Undirected Network ◽  
Centrality Metrics ◽  
The Us ◽  
Centrality Analysis

We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to the four commonly studied centrality metrics: degree, eigenvector, betweenness and closeness. We observe the states of Missouri and Maine to be respectively the most central state and the least central state with respect to all the four centrality metrics. The degree distribution is bi-modal Poisson. The eigenvector and closeness centralities also exhibit Poisson distribution, while the betweenness centrality exhibits power-law distribution. We observe a higher correlation in the ranking of vertices based on the degree centrality and betweenness centrality.

scholarly journals Amplification of pulsatile glucagon counterregulation by switch-off of α-cell-suppressing signals in streptozotocin-treated rats

2008 ◽  
Vol 295 (3) ◽  
pp. E575-E585 ◽  
Author(s):  
Leon S. Farhy ◽  
Zhongmin Du ◽  
Qiang Zeng ◽  
Paula P. Veldhuis ◽  
Michael L. Johnson ◽  
...  
Keyword(s):  
Network Modeling ◽  
Feedback Regulation ◽  
Saline Group ◽  
Proof Of Concept ◽  
Reciprocal Interactions ◽  
Modeling Analysis ◽  
Feedback Network ◽  
Male Wistar Rats ◽  
Study Support ◽  
Interdisciplinary Study

Glucagon counterregulation (GCR) is a key protection against hypoglycemia that is compromised in diabetes via an unknown mechanism. To test the hypothesis that α-cell-inhibiting signals that are switched off during hypoglycemia amplify GCR, we studied streptozotocin (STZ)-treated male Wistar rats and estimated the effect on GCR of intrapancreatic infusion and termination during hypoglycemia of saline, insulin, and somatostatin. Times 10 min before and 45 min after the switch-off were analyzed. Insulin and somatostatin, but not saline, switch-off significantly increased the glucagon levels ( P = 0.03), and the fold increases relative to baseline were significantly higher ( P < 0.05) in the insulin and somatostatin groups vs. the saline group. The peak concentrations were also higher in the insulin (368 pg/ml) and somatostatin (228 pg/ml) groups vs. the saline (114 pg/ml) group ( P < 0.05). GCR was pulsatile in most animals, indicating a feedback regulation. After the switch-off, the number of secretory events and the total pulsatile production were lower in the saline group vs. the insulin and somatostatin groups ( P < 0.05), indicating enhancement of glucagon pulsatile activity by insulin and somatostatin compared with saline. Network modeling analysis demonstrates that reciprocal interactions between α- and δ-cells can explain the amplification by interpreting the GCR as a rebound response to the switch-off. The model justifies experimental designs to further study the intrapancreatic network in relation to the switch-off phenomenon. The results of this proof-of-concept interdisciplinary study support the hypothesis that GCR develops as a rebound pulsatile response of the intrapancreatic endocrine feedback network to switch-off of α-cell-inhibiting islet signals.

scholarly journals Assortativity Analysis of Real-World Network Graphs based on Centrality Metrics

2016 ◽  
Vol 9 (3) ◽  
pp. 7 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Font Size ◽  
Network Graph ◽  
Node Level ◽  
Centrality Metrics ◽  
Empirical Probability ◽  
Different Levels

<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Assortativity index (<em>A. Index</em>) of real-world network graphs has been traditionally computed based on the degree centrality metric and the networks were classified as assortative, dissortative or neutral if the <em>A. Index</em> values are respectively greater than 0, less than 0 or closer to 0. In this paper, we evaluate the <em>A. Index</em> of real-world network graphs based on some of the commonly used centrality metrics (betweenness, eigenvector and closeness) in addition to degree centrality and observe that the assortativity classification of real-world network graphs depends on the node-level centrality metric used. We also propose five different levels of assortativity (strongly assortative, weakly assortative, neutral, weakly dissortative and strongly dissortative) for real-world networks and the corresponding range of <em>A. Index</em> value for the classification. We analyze a collection of 50 real-world network graphs with respect to each of the above four centrality metrics and estimate the empirical probability of observing a real-world network graph to exhibit a particular level of assortativity. We claim that a real-world network graph is more likely to be neutral with respect to the betweenness and degree centrality metrics and more likely to be assortative with respect to the eigenvector and closeness centrality metrics.</span></p>

scholarly journals Lyric Text Mining Of Dangdut: Visualizing The Selected Words And Word Pairs Of The Legendary Rhoma Irama’s Dangdut Song In The 1970s Era

2018 ◽  
Vol 4 (2) ◽  
pp. 9-17
Author(s):  
Tresna Maulana Fahrudin ◽  
Ali Ridho Barakbah
Keyword(s):  
Text Mining ◽  
Data Extraction ◽  
Way Of Life ◽  
Network Graph ◽  
Human Right ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Song Lyrics ◽  
The Relationship ◽  
Word Frequencies

Dangdut is a new genre of music introduced by Rhoma Irama, Indonesian popular musician who was the Legendary dangdut singer in the 1970s era until now. The expression of  Rhoma Irama’s lyric has themes of the human being, the way of life, love, law and human right, tradition, social equality, and Islamic messages. But interestingly, the song lyrics were written by Rhoma Irama in the 1970s were mostly on the love song themes. In order to prove this, it is necessary to identify the songs through several approaches to explore the selected word and the relationship between word pairs. If each Rhoma Irama’s lyric is identified in text mining field, the lyric text extraction will be an interesting knowledge pattern. We collected the lyric from web were used as datasets, and then we have done the data extraction to store the component of lyric including the part and line of the song. We successfully applied the most word frequencies in the form of data visualization including bar chart, word cloud, term frequency-inverse document frequency, and network graph. As a results, several word pairs that often was used by Rhoma Irama in writing his song including heart-love (19 lines), heart-longing (13 lines), heart-beloved (12 lines), love-beloved (12 lines), love-longing (11 lines).

Centrality Analysis of the United States Network Graph

2019 ◽  
pp. 312-323
Author(s):  
Natarajan Meghanathan
Keyword(s):  
United States ◽  
Betweenness Centrality ◽  
The United States ◽  
Power Law Distribution ◽  
Network Graph ◽  
Central State ◽  
Undirected Network ◽  
Centrality Metrics ◽  
The Us ◽  
Centrality Analysis

The authors model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. They determine a ranking of the states in the US with respect to the four commonly studied centrality metrics: degree, eigenvector, betweenness, and closeness. They observe the states of Missouri and Maine to be, respectively, the most central state and the least central state with respect to all the four centrality metrics. The degree distribution is bi-modal Poisson. The eigenvector and closeness centralities also exhibit Poisson distribution, while the betweenness centrality exhibits power-law distribution. The authors observe a higher correlation in the ranking of vertices based on the degree centrality and betweenness centrality.

scholarly journals Docencia universitaria saludable: Una metamorfosis impostergable

2016 ◽  
Vol 20 (2) ◽  
pp. 1 ◽  
Author(s):  
Ileana Castillo-Cedeño ◽  
Luz Emilia Flores-Davis ◽  
Giselle Miranda-Cervantes ◽  
Susana Murillo-León
Keyword(s):  
Postgraduate Education ◽  
Research Process ◽  
Research And Teaching ◽  
National University ◽  
Academic Units ◽  
The University ◽  
Interdisciplinary Study ◽  
Interdisciplinary Process ◽  
Different Levels ◽  
Structured Questionnaire

This article belongs to an interdisciplinary process developed at the National University of Abstract. This article arises from the research process of the Healthy Pedagogy project at the Center for Research and Teaching in Education (CIDE, by its acronym in Spanish) of the National University of Costa Rica (UNA, by its acronym in Spanish). The Center is well known as the country’s teacher training school. CIDE is structured in 5 academic units or departments, that attend the undergraduate and postgraduate education at different levels of the education system. The research recovers the issue of health, as a crucial element of social and educational praxis. The Center relies on pedagogy as a science that supports the welfare of life in an holistic dimension. This is an interdisciplinary study that emerges from one of the objectives of the research project: identify healthy educational experiences in CIDE by applying a semi-structured questionnaire to 19 teachers from the 5 academic units. An integrated multimodal approach is used, quantitative and qualitative aspects are combined, because they offer an opportunity to explore, describe and contribute to the interpretation of what healthy means in light of the participants perceptions. The results suggest that there is a diversity of criteria regarding the meaning of healthy and how to generate appropiate personal and professional environments. This allows to identify the challenge, both for CIDE and the University, of approaching the health issue by articulating policies that promote healthy lifestyles inside and outside the classrooms in order to treat the educational community with social responsibility. 

scholarly journals Secure Data Compression Scheme for Scalable Data in Dynamic Data Storage Environments

2019 ◽  
pp. 229-237
Author(s):  
K. Suvetha Bharathi ◽  
K. Palanivel
Keyword(s):  
Data Storage ◽  
Data Transfer ◽  
Data Deduplication ◽  
Compression Scheme ◽  
Cloud Data ◽  
Fine Grained ◽  
Outsourced Data ◽  
Space Saving ◽  
Semantic Security ◽  
Different Levels

With the continuous and exponential increase of the number of users and the size of their data, data deduplication becomes more and more a necessity for cloud storage providers. By storing a unique copy of duplicate data, cloud providers greatly reduce their storage and data transfer costs. These huge volumes of data need some practical platforms for the storage, processing and availability and cloud technology offers all the potentials to fulfill these requirements. Data deduplicationis referred to as a strategy offered to data providers to eliminate the duplicate data and keeps only a single unique copy of it for storage space saving purpose. This paper, presents a scheme that permits a more fine-grained trade-off. The intuition is that outsourced data may require different levels of protection, depending on how popular content is shared by many users. A novel idea is presented that differentiates data according to their popularity. Based on this idea, an encryption scheme is designed that guarantees semantic security for unpopular data and also provides the higher level security to the cloud data. This way, data de-duplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. Also, the backup recover system can be used at the time of blocking and also analyze frequent login access system.

scholarly journals Algorithms for Geometrical Models in Borromini's San Carlino alle Quattro Fontane

2016 ◽  
pp. 642-665 ◽  
Author(s):  
Corrado Falcolini
Keyword(s):  
Mathematical Models ◽  
Point Cloud ◽  
Measured Data ◽  
Point Cloud Data ◽  
Simple Point ◽  
Parametric Curves ◽  
Cloud Data ◽  
Curves And Surfaces ◽  
Base Curve ◽  
Different Levels

Construction of mathematical models of the vault of Borromini's San Carlino alle Quattro Fontane based on parametric curves and surfaces, including the shape of the vault and rules for its tessellation with crosses and octagonal coffers. Several models of different complexity are optimized and tested measuring their distance from the point cloud of a very accurate 3D survey and the analysis of such measured data is proposed to validate hypothesis of construction procedures by checking symmetries of coffers shape, scale and position in different levels and sectors. Some original algorithms are discussed to produce regular tessellations on a surface with a generic base curve and to construct regular parametric curves section out of simple point cloud data.

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