KONECT: the Koblenz network collection
By J´erˆome Kunegis. The Web Conference, 2013
1,784 citations recorded by the source corpus.
Subfields: Experimental and Cognitive Psychology, Molecular Biology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f816c7407ee34eab3d40d87e286ff8e1608d3d19
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Experimental and Cognitive Psychology
- Peter Forsyth
- Aram Galstyan
- Greg Ver Steeg
- Pascal Poupart
- Junming Yin
- Dong Guo
- Seyed Mehran Kazemi
- Linhong Zhu
In Molecular Biology
- Peter Forsyth
- Aram Galstyan
- Peter Sanders
- Greg Ver Steeg
- Henning Meyerhenke
- Pascal Poupart
- Junming Yin
- Dong Guo
In Statistical and Nonlinear Physics
- Peter Sanders
- Christian Schulz
- Peter Forsyth
- Aram Galstyan
- Greg Ver Steeg
- Henning Meyerhenke
- Pascal Poupart
- Junming Yin
Citations in the corpus
- Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “UCI 26 : This dataset [129] is obtained from the same social network as the one for FB-FORUM.”
- Quantification of network structural dissimilarities (2017, neutral): “All networks are freely available at The Koblenz Network Collection 49 (see description in Supplementary Note 8).”
- Recent Advances in Graph Partitioning (2013, background): “There are many other very valuable sources of graphs for experimental evaluations: the 10th DIMACS Implementation Challenge [12,13], the Florida Sparse Matrix Collection [46], the Laboratory of Web Algorithms [220], the Koblenz Network Collection [123], and the Stanford Large Network Dataset Collect”
- Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, methodology): “We use five real temporal datasets in our experiments, which are obtained from the Koblenz Large Network Collection [33].”