scholarly journals A traffic analysis attack to compute social network measures

2018 ◽  
Vol 78 (21) ◽  
pp. 29731-29745
Author(s):  
Alejandra Guadalupe Silva Trujillo ◽  
Ana Lucila Sandoval Orozco ◽  
Luis Javier García Villalba ◽  
Tai-Hoon Kim
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kylie King ◽  
Tracy Sweet

Purpose This study aims to explore how social networks could be used in the measurement of transactive memory systems (TMS) or other team constructs and provide motivation for future analyses of TMS measurement. Design/methodology/approach TMSs describe the structures and processes that teams use to share information, work together and accomplish shared goals. This paper proposes the use of social network analysis in measuring TMS. This is accomplished by describing the creation and administration of a TMS network instrument and evaluating the relation of the proposed network measures, previous measures of TMS and performance. Findings Findings include that proposed network measures perform similarly to previously proposed, frequently used measures of TMS. Originality/value To the best of the authors’ knowledge, this is among the first papers to propose network measures for the evaluation of TMS.


BioScience ◽  
2017 ◽  
Vol 67 (3) ◽  
pp. 245-257 ◽  
Author(s):  
Matthew J. Silk ◽  
Darren P. Croft ◽  
Richard J. Delahay ◽  
David J. Hodgson ◽  
Mike Boots ◽  
...  

2019 ◽  
Vol 24 (6) ◽  
pp. 821-854 ◽  
Author(s):  
Sunil Babbar ◽  
Xenophon Koufteros ◽  
Ravi S. Behara ◽  
Christina W.Y. Wong

Purpose This study aims to examine publications of supply chain management (SCM) researchers from across the world and maps the leadership role of authors and institutions based on how prolific they are in publishing and on network measures of centrality while accounting for the quality of the outlets that they publish in. It aims to inform stakeholders on who the leading SCM scholars are, their primary areas of SCM research, their publication profiles and the nature of their networks. It also identifies and informs on the leading SCM research institutions of the world and where leadership in specific areas of SCM research is emerging from. Design/methodology/approach Based on SCM papers appearing in a set of seven leading journals over the 15-year period of 2001-2015, publication scores and social network analysis measures of total degree centrality and Bonacich power centrality are used to identify the highest ranked agents in SCM research overall, as well as in some specific areas of SCM research. Social network analysis is also used to examine the nature and scope of the networks of the ranked agents and where leadership in SCM research is emerging from. Findings Authors and institutions from the USA and UK are found to dominate much of the rankings in SCM research both by publication score and social network analysis measures of centrality. In examining the networks of the very top authors and institutions of the world, their networks are found to be more inward-looking (country-centric) than outward-looking (globally dispersed). Further, researchers in Europe and Asia alike are found to exhibit significant continental inclinations in their network formations with researchers in Europe displaying greater propensity to collaborate with their European-based counterparts and researchers in Asia with their Asian-based counterparts. Also, from among the journals, Supply Chain Management: An International Journal is found to exhibit a far more expansive global reach than any of the other journals. Research limitations/implications The journal set used in this study, though representative of high-quality SCM research outlets, is not exhaustive of all potential outlets that publish SCM research. Further, the measure of quality that this study assigns to the various publications is based solely on a publication score that accounts for the quality of the journals, as rated by Association of Business Schools that the papers appear in and nothing else. Practical implications By informing the community of stakeholders of SCM research about the top-ranked SCM authors, institutions and countries of the world, the nature of their networks, as well as what the primary areas of SCM research of the leading authors in the world are, this research provides stakeholders, including managers, researchers and students, information that is helpful to them not only because of the insights it provides but also for the gauging of potential for embedding themselves in specific networks, engaging in collaborative research with the leading agents or pursuing educational opportunities with them. Originality/value This research is the first of its kind to identify and rank the top SCM authors and institutions from across the world using a representative set of seven leading SCM and primary OM journals based on publication scores and social network measures of centrality. The research is also the first of its kind to identify and rank the top authors and institutions within specific areas of SCM research and to identify future research opportunities relating to aspects of collaboration and networking in research endeavors.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Karikalan Nagarajan ◽  
Malaisamy Muniyandi ◽  
Bharathidasan Palani ◽  
Senthil Sellappan

Abstract Background Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. Methods Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as “nodes” and their epidemiological contact as “edges” was created. Directed network perspective was utilized in which directionality of infection emanated from a “source patient” towards a “target patient”. Network measures of “ degree centrality” and “betweenness centrality” were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. Results Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient’s which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. Conclusions Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.


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