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Patrick Kenis

dblp:41/5850 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2009
0000-0002-2494-6090ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Web and social media mining · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and social media mining › scholarly data mining
collaboration network analysis
0.112009
Network analysis of collaboration structure in Wikipedia · WWW 2009
Visualization and visual analytics
graph visualization
0.012003
Communicating Centrality in Policy Network Drawings · IEEE Trans. Vis. Comput. Graph. 2003
Web and social media mining › user-generated content
wikipedia
0.012009
Network analysis of collaboration structure in Wikipedia · WWW 2009

Methods — techniques the papers use, named apart from their topics

network analysis · 0.1structural-to-geometric centrality mapping · 0.1layout model · 0.1
YearPublicationVenuePosition
2009 Network analysis of collaboration structure in Wikipedia
abstract
published
Ulrik Brandes, Patrick Kenis, Jürgen Lerner, Denise van Raaij
WWW2
2003 Communicating Centrality in Policy Network Drawings
abstract
We introduce a network visualization technique that supports an analytical method applied in the social sciences. Policy network analysis is an approach to study policy making structures, processes, and outcomes, thereby concentrating on relations between policy actors. An important operational concept for the analysis of policy networks is the notion of centrality, i.e., the distinction of actors according to their importance in a relational structure. We integrate this measure in a layout model for networks by mapping structural to geometric centrality. Thus, centrality values and network data can be presented simultaneously and explored interactively.
Ulrik Brandes, Patrick Kenis, Dorothea Wagner
IEEE Trans. Vis. Comput. Graph.2
1999 Centrality in Policy Network Drawings
Ulrik Brandes, Patrick Kenis, Dorothea Wagner
GD2