scholarly journals Hacia la complejidad por la vía de las redes. Nuevas lecciones epistemológicas

2014 ◽  
Author(s):  
Carlos Reynoso
Keyword(s):  
Social Sciences ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scale Free Network ◽  
New Paradigm ◽  
Scale Free ◽  
Network Research ◽  
Free Network

This paper sur veys the reciprocal impacts between Social Network Analysis and the new paradigm of complexity and chaos theories, as well as the emergence of scale-free network research in the twenty-first centur y. This study is embedded in the context of a histor y of the most momentous events in network theor y and practice , from Euler to Barabási, used as a star ting point to interrogate some critical epistemological issues from the viewpoint of contemporar y social sciences.

Thick Big Data

2021 ◽  
pp. 210-228
Author(s):  
Dariusz Jemielniak ◽  
Agata Stasik
Keyword(s):  
Social Sciences ◽  
Qualitative Research ◽  
Big Data ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Narrative Analysis ◽  
Digital Data ◽  
Online Research ◽  
Practical Advice

As we witness a radical increase in the volume and variety of digital data, it should not come as a surprise that social sciences have become increasingly ‘datafied’. The traditional social sciences, such as sociology or anthropology, are thus under the threat of becoming marginalized or even irrelevant because of the prevalence of the new methods of research, which require more computational skills. This chapter describes a way for researchers to enter this new domain and keep their advantage of mastering qualitative research relevant: a new, mixed-method of Thick Big Data, relying on a combination of quantitative approaches (data scraping, Social Network Analysis, culturomics, sentiment analysis) with qualitative ones (digital ethnography, narrative analysis, cultural studies). The chapter outlines how these approaches may blend, and offers some practical advice for a researcher without coding skills on how to take the first steps in online research, through examples focused on Wikipedia.

Social network analysis in supply chain management research

2016 ◽  
Vol 46 (8) ◽  
pp. 740-762 ◽  
Author(s):  
Barbara K. Wichmann ◽  
Lutz Kaufmann
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Supply Chain Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Chain Management ◽  
Content Type ◽  
Network Research ◽  
The Many ◽  
The Right

Purpose The purpose of this paper is to investigate when and how to best use social network analysis (SNA) in the supply chain management (SCM) discipline. In doing so, the study identifies SCM phenomena that have been examined from a social network perspective (SNA approach) in the SCM literature and highlights additional SCM phenomena that would be worth investigating using social network research. Then, the study critically investigates the application of SNA as a methodology (SNA method), with the goal of assessing and mitigating methodological risks in future studies. Design/methodology/approach This study carries out a systematic literature review of articles published in 11 top-tier SCM journals over a 20-year period. Findings First, while social network research has gained momentum especially since 2010, scholars are not yet entirely aware of the many possibilities the SNA approach offers to the SCM field. Second, expanded possibilities also hold for the development of SNA as a method. Originality/value The paper guides future SCM research by investigating when SNA is the right approach to use and how SNA as a method should be performed. Theoretically richer and practically more relevant research should result.

scholarly journals Effectiveness of Closure of Public Places with Time Delay in Disease Control

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhenggang Wang ◽  
Kwok Yip Szeto ◽  
Frederick Chi-Ching Leung
Keyword(s):  
Social Network ◽  
Time Delay ◽  
Disease Control ◽  
Sir Model ◽  
Scale Free Network ◽  
Public Places ◽  
Scale Free ◽  
The Social ◽  
Free Network ◽  
High Degree

SummaryA theoretical basis for the evaluation of the effciency of quarantine measure is developed in a SIR model with time delay. In this model, the effectiveness of the closure of public places such as schools in disease control, modeled as a high degree node in a social network, is evaluated by considering the effect of the time delay in the identification of the infected. In the context of the SIR model, the relation between the number of infectious individuals who are identified with time delay and then quarantined and those who are not identified and continue spreading the virus are investigated numerically. The social network for the simulation is modeled by a scale free network. Closure measures are applied to those infected nodes with high degrees. The effectiveness of the measure can be controlled by the present value of the critical degree K

Hub Agents Determine Collective Behavior

2015 ◽  
Vol 11 (02) ◽  
pp. 165-181
Author(s):  
Saori Iwanaga ◽  
Akira Namatame
Keyword(s):  
Social Network ◽  
Collective Behavior ◽  
Traffic Congestion ◽  
Scale Free Network ◽  
Collective Phenomena ◽  
Computing Technology ◽  
Scale Free ◽  
Cooperative Relationships ◽  
The Social ◽  
Free Network

There are growing interests for studying collective behavior including the dynamics of markets, the emergence of social norms and conventions and collective phenomena in daily life such as traffic congestion. In our previous work [Iwanaga and Namatame, Collective behavior and diverse social network, International Journal of Advancements in Computing Technology 4(22) (2012) 321–320], we showed that collective behavior in cooperative relationships is affected in the structure of the social network, the initial collective behavior and diversity of payoff parameter. In this paper, we focus on scale-free network and investigate the effect of number of interactions on collective behavior. And we found that choices of hub agents determine collective behavior.

Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags

2014 ◽  
Vol 38 (2) ◽  
pp. 232-247 ◽  
Author(s):  
Ma Feicheng ◽  
Li Yating
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Small World ◽  
Online Resources ◽  
Social Tagging ◽  
The Internet ◽  
Content Type ◽  
Categorical Analysis ◽  
Scale Free

Purpose – This paper aims to explore the characteristics of the co-occurrence network of online tags and propose new approaches of applying social network analysis by utilising social tagging in order to organise data. Design/methodology/approach – The authors collected online resources labelled “tag” from 7 November 2004 to 31 October 2011 from the CiteULike website, comprising 684 papers and their URLs, titles and data on tagging (users, times, and tags). They examined the co-occurrence network of online tags by using the analyses of social networks, including the analysis of coherence, the analysis of centricity and core to periphery categorical analysis. Findings – Some features of the co-occurrence of online tags are as follows: the internet is subject to the “small world” phenomenon, as well as being “scale-free”. The structure of the internet reflects stable areas of core knowledge. In addition to five possible applications of social network analysis, social tagging has the greatest significance in organising online resources. Originality/value – This research finds that co-occurrence of tags online is an effective way to organise and index data. Some suggestions are provided on the organisation of online resources.

Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism

2017 ◽  
Vol 5 (6) ◽  
pp. 571-584 ◽  
Author(s):  
Jianhong Chen ◽  
Qinghua Song ◽  
Zhiyong Zhou
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Propagation Model ◽  
Immune Mechanism ◽  
Scale Free Network ◽  
Propagation Process ◽  
Agent Based ◽  
Scale Free ◽  
Rumor Propagation ◽  
Free Network

AbstractTo simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.

scholarly journals Trends and perspectives on the use of social network analysis in behavioural ecology: a bibliometric approach

2018 ◽  
Author(s):  
Quinn M.R. Webber ◽  
Eric Vander Wal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Temporal Trends ◽  
Future Research ◽  
Network Research ◽  
Emerging Trends ◽  
Primary Finding ◽  
Over Time

AbstractThe increased popularity and improved accessibility of social network analysis has improved our ability to test hypotheses about the complexity of animal social structure. To gain a deeper understanding of the use and application of social network analysis, we systematically surveyed the literature and extracted information on publication trends from articles using social network analysis. We synthesize trends in social network research over time and highlight variation in the use of different aspects of social network analysis. Our primary finding highlights the increase in use of social network analysis over time and from this finding, we observed an increase in the number of review and methods of social network analysis. We also found that most studies included a relatively small number (median = 15, range = 4–1406) of individuals to generate social networks, while the number and type of social network metrics calculated in a given study varied zero to nine (median = 2, range 0–9). The type of data collection or the software programs used to analyze social network data have changed; SOCPROG and UCINET have been replaced by various R packages over time. Finally, we found strong taxonomic and conservation bias in the species studied using social network analysis. Most species studied using social networks are mammals (111/201, 55%) or birds (47/201, 23%) and the majority tend to be species of least concern (119/201, 59%). We highlight emerging trends in social network research that may be valuable for distinct groups of social network researchers: students new to social network analysis, experienced behavioural ecologists interested in using social network analysis, and advanced social network users interested in trends of social network research. In summary we address the temporal trends in social network publication practices, highlight potential bias in some of the ways we employ social network analysis, and provide recommendations for future research based on our findings.

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