Galex: Exploring the Evolution and Intersection of Disciplines

Li, Zeyu; Zhang, Changhong; Jia, Shichao; Zhang, Jiawan

doi:10.1109/tvcg.2019.2934667

Cited by 15 publications

(4 citation statements)

References 54 publications

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“…In contrast to our work, however, the collaboration structure of the publication network and the individual expertise of the authors were not taken into account in the authors' approach. A similar distinction applies to Li et al (2019), though in their paper the authors considered topical co-authorship to be a relevant variable.…”

Section: Related Workmentioning

confidence: 96%

Research topic flows in co-authorship networks

2022

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In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

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Section: Related Workmentioning

confidence: 96%

Research topic flows in co-authorship networks

2022

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“…For image or text, it is a simple and effective way to interpret their DR results by directly [35] or interactively [15,20,22] drawing the original data as annotation in reduced space. For other types of highdimensional data, Ji et al [17] applied parallel coordinates plots to identify hidden semantic features associated with recognized clusters.…”

Section: Understanding the Dr Resultsmentioning

confidence: 99%

SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis

Zhang

et al. 2021

J Vis

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“…But contrary to existing approaches 63,64 , our system will display more complex inheritance lineages with direct parents / children as well as distant ancestors. Our system will have the particularity to propose an endogenous and hierarchical branches' layout that rests on individual term-to-term temporal relations at a given level (see 3.4) instead of using pre-existing ontologies 67 or top-down topics clustering 65,79 . Finally, we will take advantage of the synchronic multi-scale ranges of description induced by the choice of a given level to fold / unfold the content of a branch without additional calculation (see 3.5) like re-processing tree cuts 65 or still using pre-existing organizations 70 .…”

Section: Revealing the Dynamics Of Knowledgementioning

confidence: 99%

Exploring, browsing and interacting with multi-level and multi-scale dynamics of knowledge

Lobbé¹,

Delanoë²,

Chavalarias

2021

Information Visualization

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The ICT revolution has given birth to a world of digital traces. A wide number of knowledge-driven domains like science are daily fueled by unlimited flows of textual contents. In order to navigate across these growing constellations of words, interdisciplinary innovations are emerging at the crossroad between social and computational sciences. In particular, complex systems approaches make it now possible to reconstruct multi-level and multi-scale dynamics of knowledge by means of inheritance networks of elements of knowledge called phylomemies. In this article, we will introduce an endogenous way to visualize the multi-level and multi-scale properties of phylomemies. The resulting system will enrich a state-of-the-art tree like representation with the possibility to browse through the evolution of a corpus of documents at different level of observation, to interact with various scales of description, to reconstruct a hierarchical clustering of elements of knowledge and to navigate across complex semantic lineages. We will then formalize a generic macro-to-micro methodology of exploration and implement our system as a free software called the Memiescape. Our system will be illustrated by three use cases that will respectively reconstruct the scientific landscape of the top cited publications of the French CNRS, the evolution of the state of the art of knowledge dynamics visualization and the ongoing discovery process of Covid-19 vaccines.

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Galex: Exploring the Evolution and Intersection of Disciplines

Cited by 15 publications

References 54 publications

Research topic flows in co-authorship networks

Research topic flows in co-authorship networks

SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis

Exploring, browsing and interacting with multi-level and multi-scale dynamics of knowledge

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