Application of mining social network graph on the ocean energy system

2020 ◽  
pp. 329-346
Author(s):  
Vikas Khare ◽  
Savita Nema ◽  
Prashant Baredar
Keyword(s):  
Social Network ◽  
Energy System ◽  
Ocean Energy ◽  
Network Graph

Creating “(Social) Network Art” with NodeXL

2017 ◽  
pp. 342-393
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Network ◽  
Network Analysis ◽  
Web 2.0 ◽  
Data Visualization ◽  
Network Graph ◽  
Social Media Platforms ◽  
Machine Art ◽  
Social Sharing

If human-created objects of art are historically contingent, then the emergence of (social) network art may be seen as a product of several trends: the broad self-expression and social sharing on Web 2.0; the application of network analysis and data visualization to understand big data, and an appreciation for online machine art. Social network art is a form of cyborg art: it melds data from both humans and machines; the sensibilities of humans and machines; and the pleasures and interests of people. This chapter will highlight some of the types of (social) network art that may be created with Network Overview, Discovery and Exploration for Excel (NodeXL Basic) and provide an overview of the process. The network graph artwork presented here were all built from datasets extracted from popular social media platforms (Twitter, Flickr, YouTube, Wikipedia, and others). This chapter proposes some early aesthetics for this type of electronic artwork.

Exploiting social network graph characteristics for efficient BFS on heterogeneous chips

2018 ◽  
Vol 120 ◽  
pp. 282-294 ◽  
Author(s):  
Luis Remis ◽  
Maria Jesus Garzaran ◽  
Rafael Asenjo ◽  
Angeles Navarro
Keyword(s):  
Social Network ◽  
Network Graph

Utility-aware social network graph anonymization

2015 ◽  
Vol 56 ◽  
pp. 137-148 ◽  
Author(s):  
Mohd Izuan Hafez Ninggal ◽  
Jemal H. Abawajy
Keyword(s):  
Social Network ◽  
Network Graph

scholarly journals A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

2019 ◽  
Vol 8 (6) ◽  
pp. 933-937
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Linear Time ◽  
A Priori ◽  
Label Propagation ◽  
Data Sets ◽  
Network Graph ◽  
Priori Information

Identifying communities has always been a fundamental task in analysis of complex networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. Amongst them, the label propagation algorithm (LPA) brings great scaslability together with high accuracy but which is not accurate enough because of its randomness. In this paper, we study the equivalence properties of nodes on social network graphs according to the labeling criteria to shorten social network graphs and develop label propagation algorithms on shortened graphs to discover effective social networking communities without requiring optimization of the objective function as well as advanced information about communities. Test results on sample data sets show that the proposed algorithm execution time is significantly reduced compared to the published algorithms. The proposed algorithm takes an almost linear time and improves the overall quality of the identified community in complex networks with a clear community structure.

scholarly journals To the evolution model of network graph structure construction

2021 ◽  
pp. 1-16
Author(s):  
Yurii Nikolaevich Orlov ◽  
Alexander Seraphimovich Pankratov
Keyword(s):  
Social Network ◽  
High Dimension ◽  
Liouville Equation ◽  
Evolution Model ◽  
Structure Evolution ◽  
Graph Structure ◽  
Network Graph ◽  
The Social

In this paper the investigation of the structure of network graph is presented. The social network between the Russian towns is considered. It is shown, that the distribution of vertex powers is uniform. As a consequence there is a high dimension region with whole connection. The probability of special sub-graphs is estimated. The Liouville equation is used for modeling of the graph structure evolution.

scholarly journals Current Policy and Technology for Tidal Current Energy in Korea

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1807 ◽  
Author(s):  
Ko ◽  
Chung ◽  
Lee ◽  
Park ◽  
Yi
Keyword(s):  
Energy Consumption ◽  
Energy Policy ◽  
Tidal Current ◽  
Negative Impact ◽  
Energy Resource ◽  
Energy System ◽  
Current Status ◽  
Ocean Energy ◽  
Tidal Current Energy ◽  
Current Energy

As global energy consumption continues to increase, the negative impact of global warming also grows. Therefore, eco-friendly energy policy is being established all over the world. Korea’s energy consumption problems are further complicated by the country’s high dependence on energy sourced overseas. Korean energy policy is evolving rapidly to address these problems. Korea has begun to phase out nuclear power and is focusing on developing new sources of renewable energy. So there has been substantial interest in the development of ocean energy. Of all ocean energy technologies, tidal current energy is the closest to the commercialization. Especially, the southwestern coast of Korea is the most promising candidate site for the development of tidal current energy owing to the abundant tidal current energy resource. Meanwhile, ocean energy policy is an important factor in determining the development of ocean energy. Thus, this paper presents the overview of the current status of policy and technology for the tidal current energy system in Korea. These policies explained in this paper can provide significant interest and motivation for the use of tidal current energy resources.

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