Time-aware link prediction to explore network effects on temporal knowledge evolution

Choudhury, Nazim; Uddin, Shahadat

doi:10.1007/s11192-016-2003-5

Cited by 44 publications

(10 citation statements)

References 58 publications

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“…The analysis consists of building a network of associated words to measure the intensity of the association between two words (co-occurrence). Based on analysis derived from social networks and the concept of centrality from network theory, it is possible to observe the evolution of the content of a set of definitions (Choudhury and Uddin, 2016). We used co-word analysis to analyze the structure of the definition of the concept.…”

Section: Methodsmentioning

confidence: 99%

The origin and evolution of the concept of servitization: a co-word and network analysis

Pinillos

Díaz‐Garrido

Peña

2021

JBIM

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Purpose The purpose of this paper is to analyze the origins and evolution of the concept of servitization by studying the definitions of servitization provided in the literature. Servitization represents an academic field that has grown rapidly since its inception. However, the conceptualization of servitization varies greatly, in part because of the number of studies on this topic and the fact that it has been analyzed in a range of disciplines using a number of theoretical approaches. There is a need to standardize the vocabulary to create a general definition that can support the development of theory in this domain and help legitimize servitization as a research area. Design/methodology/approach This study conducts a systematic, quantitative analysis of a broad set of definitions of servitization. Specifically, this study performs content analysis (combining co-word analysis and social network analysis) and consensus analysis. This study develops a strategic diagram to represent the morphology of the research network. Findings The definitions of servitization are deconstructed and analyzed in depth to create a comprehensive picture of the research on this topic. This analysis reveals the origins and evolution of this research area. The results show a low degree of consensus among scholars regarding the concept of servitization. This study proposes a definition that should be widely accepted thanks to its inclusion of the core terms from other definitions. Explicit recognition of multiple approaches to defining the term can help practitioners and researchers. Predictions about future progress in this area are discussed. Originality/value A universal definition of servitization is proposed based on the results of co-word and network analysis. This definition unifies a range of multidisciplinary viewpoints. From a practical perspective, the key vocabulary in servitization research is highlighted.

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Section: Methodsmentioning

confidence: 99%

The origin and evolution of the concept of servitization: a co-word and network analysis

Pinillos

Díaz‐Garrido

Peña

2021

JBIM

View full text Add to dashboard Cite

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“…ML methods were then applied to classify the links and predict future associations among keywords. Autoregressive integrated moving average model (ARIMA), a time series analysis model, was used to predict the values of topological evolution (Choudhury & Uddin, 2016 ). Main path analysis (MPA) of citation network is another very promising method for understanding the evolution of a scientific domain (Jiang et al, 2020 ; Xu et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

2022

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The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of “emerging topics” share the similar characters with the “cover papers”, this study present a novel approach to translate the “emerging topics” detection to “cover paper” prediction. By using “AdaBoost model” and topic model, we construct a machine learning framework to imitate the top journal (chief) editor’s judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.

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“…Heterogeneous networks on the other hand contain rich information embedded in the form of meta nodes and meta paths through hyperlinks [78], [72]. Simplifications by data reduction techniques will lead to inaccurate detection of community structures [61], [77], [83]. In a highly complex space, uni-dimensional measures are inadequate at describing the detection process.…”

Section: Community Detection Techniquesmentioning

confidence: 99%

On relational learning and discovery in social networks: a survey

Zhang

Tan

Tao

2018

Int. J. Mach. Learn. & Cyber.

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The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence into popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion of data in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements.

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Time-aware link prediction to explore network effects on temporal knowledge evolution

Cited by 44 publications

References 58 publications

The origin and evolution of the concept of servitization: a co-word and network analysis

The origin and evolution of the concept of servitization: a co-word and network analysis

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

On relational learning and discovery in social networks: a survey

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