Learning from the Past: An Analysis of Person Name Corrections in the DBLP Collection and Social Network Properties of Affected Entities

Author(s):  
Florian Reitz ◽  
Oliver Hoffmann
2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.


Author(s):  
Tom Brughmans ◽  
Anna Collar

As his keynote address to the 1990 Sunbelt Social Networks conference, Mark Granovetter presented a paper entitled ‘The Myth of Social Network Analysis as a Special Method in the Social Sciences’ (Granovetter 1990). In it, he described how the popular social network theory he proposed, ‘The Strength of Weak Ties’ (Granovetter 1973), was like a spectre that haunted his academic career: although he subsequently pursued other research interests, he found that ‘as I got more deeply into any subject, network ideas kept coming in the back door’. He concluded that social network analysis (SNA) is not a ‘special’ method in social science, because ‘no part of social life can be properly analysed without seeing how it is fundamentally embedded in networks of social relations’ (Granovetter 1990: 15). However, he noted that to many, SNA is an alien concept: ‘we need to remember that there are many scholars outside the house of social network analysis who think in a relational way but don’t see the kinship with network methods and ideas’ (Granovetter 1990: 15). This observation echoes the current position of network studies in archaeology and history. Few would argue that relationships between social entities are not important for understanding past social processes. However, more explicit application of network theories and methods is not yet a mainstream part of our disciplines. Although it is the case that some researchers are not aware of the advantages such perspectives might offer, the current ‘niche’ status of network applications in archaeological and historical research relates to a more general misperception: that network concepts and methodologies per se are simply not appropriate for use in research in these disciplines. This volume aims to address both issues: the contributions in this volume demonstrate both the enormous potential of network methodologies, and also—and perhaps more importantly—acknowledge and address a range of perceived problems and reservations relating to the application of network perspectives to the study of the past, thereby encouraging and enabling their wider use in archaeology and history. The full diversity of network perspectives has only been introduced in our disciplines relatively recently.


2019 ◽  
Vol 2 (1) ◽  
pp. 99-122 ◽  
Author(s):  
Katherine Faust ◽  
George E. Tita

Over the past decade, a considerable literature has emerged within criminology stemming from the collection of social network data and the adoption of social network analysis by a cadre of scholars. We review recent contributions to four areas of crime research: co-offending networks, illicit networks, gang-rivalry networks, and neighborhoods and crime. Our review highlights potential pitfalls that one might encounter when using social networks in criminological research and points to fruitful directions for further research. In particular, we recommend paying special attention to the clear specifications of what ties in the network are assumed to be doing, potential measurement weaknesses that can arise when using police or investigative data to construct a network, and understanding dynamic social network processes related to criminological outcomes. We envision a bright future in which the social network perspective will be more fully integrated into criminological theories, analyses, and applications.


Author(s):  
Kwan Yi

This proposal is the extended work in implementation of a framework of topic-centered collaboration network. A goal of this study is to investigate the question of: In which topics and in what extent researchers collaborate with others? Topic-centered collaboration networks for two scholarly journals in the field of information science are constructed using bibliographic datasets for the past five years. This proposal contributes to the areas of both collaboration social network and big metadata.


2018 ◽  
Vol 11 (4) ◽  
pp. 433-446 ◽  
Author(s):  
Fallon R. Mitchell ◽  
Sara Santarossa ◽  
Sarah J. Woodruff

The present study aimed to explore the interactions and influences that occurred on Twitter after Joey Julius’s (NCAA athlete, Penn State Football) and Mike Marjama’s (MLB player, Seattle Mariners) eating-disorder (ED) diagnoses were revealed. Corresponding with the publicizing of each athlete’s ED, all publicly tagged Twitter media using @joey_julius, Joey Julius, @MMarjama, and Mike Marjama were collected using Netlytic software and analyzed. Text analysis revealed that the conversation was supportive and focused on feelings and size. Social network analysis, based on 5 network properties, showed that Joey Julius invoked a larger conversation but that both athletes’ conversations were single sided. Athlete advocacy on social media should be further explored, as it may contribute to changing societal opinion regarding social issues such as EDs.


2020 ◽  
Vol 185 ◽  
pp. 02024
Author(s):  
Yuqing Liao ◽  
Jingliang Chen

Based on the green finance policies in China from 2017 to 2019, this paper extracts feature and high-frequency words from policy documents, uses word cloud diagram, co-occurrence matrix and social network analysis techniques to quantitatively analyse the information contained in the green finance policies over the past three years and highlights the hot issues in question, thus providing a multi-layered and wideranging pathway for facilitating the orderly development of green finance industries across China.


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