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.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

Social network analysis applications in sustainable construction and built environment management: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nandun Madhusanka Hewa Welege ◽  
Wei Pan ◽  
Mohan Kumaraswamy
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Built Environment ◽  
Methodological Approach ◽  
Relevant Literature ◽  
Sustainable Construction ◽  
Centrality Measures ◽  
Environment Management ◽  
Content Type

PurposeApplications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.Design/methodology/approach95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.FindingsA significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.Originality/valueCloser collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.

The evolution of the intellectual structure of strategic management between 1980 and 2019

2020 ◽  
Vol 13 (4) ◽  
pp. 503-534
Author(s):  
Mehmet Ali Köseoğlu ◽  
John Parnell
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Strategic Management ◽  
Citation Analysis ◽  
Intellectual Structure ◽  
Future Directions ◽  
Content Type ◽  
The Social ◽  
Economic Foundation

PurposeThe authors evaluate the evolution of the intellectual structure of strategic management (SM) by employing a document co-citation analysis through a network analysis for academic citations in articles published in the Strategic Management Journal (SMJ).Design/methodology/approachThe authors employed the co-citation analysis through the social network analysis.FindingsThe authors outlined the evolution of the academic foundations of the structure and emphasized several domains. The economic foundation of SM research with macro and micro perspectives has generated a solid knowledge stock in the literature. Industrial organization (IO) psychology has also been another dominant foundation. Its robust development and extension in the literature have focused on cognitive issues in actors' behaviors as a behavioral foundation of SM. Methodological issues in SM research have become dominant between 2004 and 2011, but their influence has been inconsistent. The authors concluded by recommending future directions to increase maturity in the SM research domain.Originality/valueThis is the first paper to elucidate the intellectual structure of SM by adopting the co-citation analysis through the social network analysis.

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.

FinTech innovation and knowledge flows in Hong Kong’s financial sector: a social network analysis approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anson Au
Keyword(s):  
Hong Kong ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Commercial Banks ◽  
Financial Sector ◽  
Knowledge Flows ◽  
Foreign Firms ◽  
Content Type ◽  
Network Position

Purpose This paper aims to examine how financial technology (FinTech) knowledge from foreign firms flows into and among elite commercial banks in Hong Kong’s financial sector to drive innovation. Design/methodology/approach Using social network analysis and regression analysis on a novel database of patents held by Hong Kong’s elite commercial banks, this paper examines the relationships between network position and FinTech knowledge flow. Findings This paper finds four untold patterns of innovation and inequality in Hong Kong’s financial sector: only three banks are responsible for all the FinTech knowledge entering Hong Kong; most foreign FinTech comes from the USA through Hong Kong and Shanghai Banking Corporation, whereas most FinTech from China enters through Fubon Bank and Development Bank of Singapore; older banks and banks with more connections to firms inside Asia are more likely to import FinTech; the most beneficial sources of FinTech for a bank’s network position are firms from outside Asia. Originality/value Despite the well-documented volumes of cross-border and cross-continental movement of financial institutions in Hong Kong, there is little work on the knowledge flows that underwrite this mobility. This paper addresses this gap by using FinTech knowledge flows to map the distribution of innovation, network position and competitive advantage in Hong Kong’s financial sector.

Social network analysis and data visualization of MIS international collaboration in Taiwan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yung-Ting Chuang ◽  
Yi-Hsi Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Collaboration ◽  
Research Productivity ◽  
Research Collaboration ◽  
Clustering Algorithms ◽  
The United States ◽  
Content Type ◽  
Collaboration Patterns

PurposeThe purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.Design/methodology/approachThe authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.FindingsThe authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.Originality/valueThis study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

Obstacles and opportunities for networked practice: a social network analysis of an inter-organizational STEM ecosystem

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Hwa Liou ◽  
Alan J. Daly
Keyword(s):  
Higher Education ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Geographic Region ◽  
Central Position ◽  
Content Type ◽  
Related Information ◽  
Stem Pipeline ◽  
And Mathematics

PurposeThis study responds to major administrative and policy priorities to support science, technology, engineering and mathematics (STEM) education by investigating a multi-sector ecosystem of regional organizations that support a STEM pipeline for education and careers.Design/methodology/approachWe use social network analysis to investigate an entire region within a geographic region of California which included 316 organizations that represent different stakeholder groups, including educational institutions (school districts, schools and higher education), government, private companies, museums, libraries and multiple community-based organizations. This STEM ecosystem reflects a systems-level analysis of a region from a unique social network perspective.FindingsResults indicate that organizations have a surface-level access to STEM-related information, but the deeper and more intense relationship which involves strategic collaboration is limited. Further, interactions around information and collaboration between organizations were purportedly in part to be about education, rarely included PK-12 schools and district as central actors in the ecosystem. In addition, while institutions of higher education occupy a central position in connecting and bridging organizations within the ecosystem, higher education's connectivity to the PK-12 education sector is relatively limited in terms of building research and practice partnerships.Originality/valueThis research has implications for how regional-level complex systems are analyzed, led and catalyzed and further reflects the need to intentionally attend to the growth of STEM networks.

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