Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The role of artificial intelligence in achieving the Sustainable Development Goals
Countries citing papers authored by Virginia Dignum
Since
Specialization
Citations
This map shows the geographic impact of Virginia Dignum's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Virginia Dignum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Virginia Dignum more than expected).
This network shows the impact of papers produced by Virginia Dignum. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Virginia Dignum. The network helps show where Virginia Dignum may publish in the future.
Co-authorship network of co-authors of Virginia Dignum
This figure shows the co-authorship network connecting the top 25 collaborators of Virginia Dignum.
A scholar is included among the top collaborators of Virginia Dignum based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Virginia Dignum. Virginia Dignum is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hellström, Thomas, Virginia Dignum, & Suna Bensch. (2020). Bias in machine learning - what is it good for?. DiVA at Umeå University (Umeå University). 3–10.2 indexed citations
King, Thomas C., Virginia Dignum, & Catholijn M. Jonker. (2016). When do rule changes count-as legal rule changes?. Oxford University Research Archive (ORA) (University of Oxford).1 indexed citations
Dignum, Virginia, Catholijn M. Jonker, Rui Prada, & Frank Dignum. (2014). Situational Deliberation : Getting to Social Intelligence. Research Repository (Delft University of Technology).1 indexed citations
10.
Dignum, Virginia, et al.. (2013). Norm compliance checking. Adaptive Agents and Multi-Agents Systems. 1121–1122.4 indexed citations
11.
Aldewereld, Huib, et al.. (2013). Dimensions of Organizational Coordination. Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU).1 indexed citations
Dignum, Frank, Virginia Dignum, & Liz Sonenberg. (2006). Exploring congruence between organizational structure and task performance: a simulation approach. Utrecht University Repository (Utrecht University).6 indexed citations
15.
Grossi, Davide, et al.. (2006). Structural Aspects of the Evaluation of Agent Organization. Data Archiving and Networked Services (DANS).1 indexed citations
16.
Dignum, Virginia, Ea Sonenberg, & Frank Dignum. (2004). Dynamic Reorganization of Agent Societies. Utrecht University Repository (Utrecht University).13 indexed citations
17.
Dignum, Virginia. (2004). An Overview of Agents in Knowledge Management. Utrecht University Repository (Utrecht University).19 indexed citations
Dignum, Virginia, J-J.Ch. Meyer, Hans Weigand, & Frank Dignum. (2002). An organization-oriented model for agent systems. Utrecht University Repository (Utrecht University).1 indexed citations
20.
Dignum, Frank & Virginia Dignum. (2001). Modelling Agent Societies: Coordination Frameworks and Institutions. Utrecht University Repository (Utrecht University).14 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.