scholarly journals Optimization of Big Data Scheduling in Social Networks

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 902 ◽  
Author(s):  
Weina Fu ◽  
Shuai Liu ◽  
Gautam Srivastava
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Communication ◽  
Data Scheduling ◽  
Scheduling Model ◽  
Entropy Measures ◽  
Network Big Data ◽  
Data Collision ◽  
Target Data

In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

scholarly journals Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

scholarly journals Determining the proximity of groups in social networks based on text analysis using big data

2019 ◽  
pp. 521-526
Author(s):  
A S Mukhin ◽  
I A Rytsarev ◽  
R A Paringer ◽  
A V Kupriyanov ◽  
D V Kirsh
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Collection ◽  
Text Analysis ◽  
Software Tool ◽  
Text Data ◽  
The Social ◽  
Information Research ◽  
Definition Of

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

2017 ◽  
Vol 13 (4) ◽  
pp. 2097-2105 ◽  
Author(s):  
Sheng Gao ◽  
Huacan Pang ◽  
Patrick Gallinari ◽  
Jun Guo ◽  
Nei Kato
Keyword(s):  
Big Data ◽  
Social Network ◽  
Information Diffusion ◽  
Embedding Method ◽  
Network Big Data ◽  
Diffusion Prediction

scholarly journals Social Network Big Data Hierarchical High-Quality Node Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dongning Jia ◽  
Bo Yin ◽  
Xianqing Huang
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Shopping Mall ◽  
Network Data ◽  
Clustering Methods ◽  
Learning Mechanisms ◽  
Social Network Data ◽  
Analysis Methods ◽  
Network Big Data

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self-organizing self-learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e-commerce platform, and cultivating their own customer base.

scholarly journals Social Network Extraction Unsupervised

2021 ◽  
Vol 12 (3) ◽  
pp. 4443-4449
Author(s):  
Mahyuddin K. M. Nasution Et.al
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Information Technology ◽  
Big Data ◽  
Social Network ◽  
Data Science ◽  
Information Sources ◽  
Scientific Data

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

scholarly journals European Communication Monitor 2016 : 10 ans de recherche pan-européenne sur la communication stratégique

2017 ◽  
pp. 181-205 ◽  
Author(s):  
Anne-Marie Cotton
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Data Communication ◽  
Strategic Communication ◽  
Know How ◽  
Réseaux Sociaux ◽  
Communication Algorithms ◽  
The Social ◽  
Communication Practices ◽  
Reseaux Sociaux

L’équipe de recherche du European Communication Monitor (ECM) publie les résultats de la dixième édition de leurs questionnements sur les développements et les dynamiques de la communication stratégique dans 43 pays d’Europe. Dans l’étude 2016 l’analyse des « big data », des algorithmes en communication, des pratiques en communication propres au coaching et au conseil, de l’engagement des parties prenantes, des influenceurs actifs sur les réseaux sociaux, et des savoir, savoir-faire et savoir-être des professionnels de la communication. 2710 professionnels de la communication ont participé à l’étude. Les nombreuses comparaisons avec les résultats de l’ECM 2013 dénoncent la faible évolution du niveau de compétences moyen des professionnels de la communication à l’exception de la prévention et la gestion des crises sur les réseaux sociaux. Dans une optique de standards de la professionnalisation, les chercheurs ont créé le « comparative excellence framework » (CEF) qui vise à identifier les caractéristiques distinguant les professionnels et identifiant les pratiques d’excellence. The research team of the European Communication Monitor (ECM) publishes the results of the tenth edition of their pan-European study on the developments and dynamics of strategic communication in 43 European countries. In the 2016 edition, they focused on the analysis of « big data », communication algorithms, communication practices specifically dealing with coaching and consulting, stakeholders engagement, active influencers on social networks, and the knowledge, skills and know-how of communication professionals. 2710 communication professionals participated in the study. The comparisons with the results of the ECM 2013 reveal a weak evolution of the average level of competences of the communication professionals with the exception of the prevention and the management of crises on the social networks. Willing to support professionalisation standards, the researchers have created the comparative excellence framework (CEF), which aims to identify the characteristics distinguishing professionals and identifying best practices.

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