Topic space trajectories

Schaefermeier, Bastian; Stumme, Gerd; Hanika, Tom

doi:10.1007/s11192-021-03931-0

Cited by 3 publications

(13 citation statements)

References 35 publications

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“…The next logical step is to enhance our maps with location semantics, e.g., recognizing a registration desk at a conference venue. One can also imagine to automatically derive topical descriptions [24] for locations based on volunteered publication data from the conference participants. As a limitation, we noticed that the device diversity problem could be addressed through more sophisticated methods.…”

Section: Discussionmentioning

confidence: 99%

Topological Indoor Mapping through WiFi Signals

Schaefermeier¹,

Stumme²,

Hanika³

2021

Preprint

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The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.

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Section: Discussionmentioning

confidence: 99%

Topological Indoor Mapping through WiFi Signals

Schaefermeier¹,

Stumme²,

Hanika³

2021

Preprint

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“…We compute topic space trajectories for authors (i.e., scientists) and venues (i.e., conferences and journals), following the approach from Schaefermeier et al [19], which we will outline briefly here. Proceeding from this, we explain the additional steps for the computation and visualization of the final interactive map.…”

Section: Methodsmentioning

confidence: 99%

“…Our work translates this idea to a "non-physical" space in which the dimensions represent research topics. We base this on our work [19], in which we analyzed topic space trajectories of scientific venues. In this work, we create a unified framework for the interactive analysis of scientific trajectories through maps.…”

Section: Related Workmentioning

confidence: 99%

“…A natural consequence is the need for automated methods and tools which support researchers in these tasks. In Schaefermeier et al [19] we developed a novel method for the examination of topic space trajectories for the special case of scientific venues (i.e., conferences or journals). This method enables the discovery and tracking of research topics in a publication corpus based on textual contents.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In more detail, we use a combination of two corpora: First, a venue corpus of papers from the top 30 machine learning and data mining venues listed in [24]. This corpus was also used in Schaefermeier et al [19]. Hence, the authors of the present work were already familiar with this data.…”

Section: Publication Corpusmentioning

confidence: 99%

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Mapping Research Trajectories

Schäfermeier¹,

Stumme²,

Hanika³

2022

Preprint

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Steadily growing amounts of information, such as annually published scientific papers, have become so large that they elude an extensive manual analysis. Hence, to maintain an overview, automated methods for the mapping and visualization of knowledge domains are necessary and important, e.g., for scientific decision makers. Of particular interest in this field is the development of research topics of different entities (e.g., scientific authors and venues) over time. However, existing approaches for their analysis are only suitable for single entity types, such as venues, and they often do not capture the research topics or the time dimension in an easily interpretable manner.Hence, we propose a principled approach for mapping research trajectories, which is applicable to all kinds of scientific entities that can be represented by sets of published papers. For this, we transfer ideas and principles from the geographic visualization domain, specifically trajectory maps and interactive geographic maps. Our visualizations depict the research topics of entities over time in a straightforward interpretable manner. They can be navigated by the user intuitively and restricted to specific elements of interest. The maps are derived from a corpus of research publications (i.e., titles and abstracts) through a combination of unsupervised machine learning methods.In a practical demonstrator application, we exemplify the proposed approach on a publication corpus from machine learning. We observe that our trajectory visualizations of 30 top machine learning venues and ∼1000 major authors in this field are well interpretable and are consistent with background knowledge drawn from the entities' publications. Next to producing interactive, interpretable visualizations supporting different kinds of analyses, our computed trajectories are suitable for trajectory mining applications in the future.

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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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Topic space trajectories

Cited by 3 publications

References 35 publications

Topological Indoor Mapping through WiFi Signals

Topological Indoor Mapping through WiFi Signals

Mapping Research Trajectories

Research topic flows in co-authorship networks

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