A Study on Scale Free Social Network Evolution Model with Degree Exponent < 2

2020 ◽  
Vol 33 (1) ◽  
pp. 87-99
Author(s):  
Zhenpeng Li ◽  
Xijin Tang
Keyword(s):  
Social Network ◽  
Network Evolution ◽  
Evolution Model ◽  
Scale Free ◽  
Social Network Evolution

A social network evolution model based on seniority

2011 ◽  
Vol 2 (2) ◽  
pp. 107-119 ◽  
Author(s):  
Yi-Kuang Ko ◽  
Jing-Kai Lou ◽  
Cheng-Te Li ◽  
Shou-De Lin ◽  
Shyh-Kang Jeng
Keyword(s):  
Social Network ◽  
Network Evolution ◽  
Evolution Model ◽  
Model Based ◽  
Social Network Evolution

Social network evolution in creative process of CNPD teams: a case study of Chinese companies

2018 ◽  
Vol 53 (1) ◽  
pp. 143-181 ◽  
Author(s):  
Wei Zhang ◽  
Yongli Li ◽  
Wenyao Zhang ◽  
Shengli Dai
Keyword(s):  
Social Network ◽  
Creative Process ◽  
Network Evolution ◽  
Chinese Companies ◽  
Social Network Evolution

scholarly journals A Novel Wireless Sensor Network Evolution Model Based on Energy-Efficiency

2017 ◽  
Vol 13 (03) ◽  
pp. 4 ◽  
Author(s):  
Hui Gao ◽  
Zhixian Yang
Keyword(s):  
Preferential Attachment ◽  
Influence Factors ◽  
Network Evolution ◽  
Evolution Model ◽  
Wireless Sensor ◽  
Font Size ◽  
Total Energy Consumption ◽  
Scale Free ◽  
Random Node ◽  
Attachment Mechanism

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">The Barabási–Albert (BA) model is a famous complex network model that generates scale-free networks. Wireless sensor networks (WSNs) had been thought to be approximately scale-free through lots of empirical research. Based on the BA model, we propose an evolution model for WSNs. According to actual influence factors such as the remainder energy of each sensor and physical link capability of each sensor, our evolution model constructs WSNs by using a preferential attachment mechanism. Through simulation and analysis, we can prove that our evolution model would make the total energy consumption of the WSNs more efficient and have a superior random node error tolerance.</span>

scholarly journals An Improved B-A Model for Scale-free Network Evolution Model

2020 ◽  
Vol 1486 ◽  
pp. 022034
Author(s):  
Hongyan Wei ◽  
Jiangong Wang ◽  
Tianqi Wang
Keyword(s):  
Network Evolution ◽  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Similarity-Based Indices or Metrics for Link Prediction

2019 ◽  
pp. 1-29
Author(s):  
Praveen Kumar Bhanodia ◽  
Kamal Kumar Sethi ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Link Prediction ◽  
Online Social Network ◽  
Dynamic Networks ◽  
Relevant Information ◽  
Network Evolution ◽  
Problem Instance ◽  
The Social ◽  
Social Network Evolution

Link prediction in social network has gained momentum with the inception of machine learning. The social networks are evolving into smart dynamic networks possessing various relevant information about the user. The relationship between users can be approximated by evaluation of similarity between the users. Online social network (OSN) refers to the formulation of association (relationship/links) between users known as nodes. Evolution of OSNs such as Facebook, Twitter, Hi-Fi, LinkedIn has provided a momentum to the growth of such social networks, whereby millions of users are joining it. The online social network evolution has motivated scientists and researchers to analyze the data and information of OSN in order to recommend the future friends. Link prediction is a problem instance of such recommendation systems. Link prediction is basically a phenomenon through which potential links between nodes are identified on a network over the period of time. In this chapter, the authors describe the similarity metrics that further would be instrumental in recognition of future links between nodes.

Analysis of Social Network Mutate in Start-Up Enterprises Evolution

2011 ◽  
Vol 181-182 ◽  
pp. 9-13
Author(s):  
Min Wang ◽  
Hua Tao Peng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Environmental Changes ◽  
Network Evolution ◽  
Reverse Direction ◽  
Start Up ◽  
Mutation Theory ◽  
Basic Concepts ◽  
Definition Of ◽  
Social Network Evolution

In the process of evolution in Start-up Enterprises, its social networks will often occurred Mutations phenomenon because of environmental changes, the changing conditions, the reverse direction, management of error, etc. This paper analyzed the evolution in social network of Start-up Enterprises through the basic concepts of the theory of mutations, definition of mutations in Start-up Enterprises′ social network evolution, and the using of swallow-tail mutation theory; proposed countermeasures of how to make full use of social network to facilitate Start-up Enterprises timely and accurately identify mutations, take measures, reduce the loss which mutations brought.

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