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We present the Koblenz Network Collection (KONECT), a project to collect network datasets in the areas of web science, network science and related areas, as well as provide tools for their analysis. In the cited areas, a surprisingly large number of very heterogeneous data can be modeled as networks and consequently, a unified representation of networks can be used to gain insight into many kinds of problems. Due to the emergence of the World Wide Web in the last decades many such datasets are now openly available. The KONECT project thus has the goal of collecting many diverse network datasets from the Web, and providing a way for their systematic study. The main parts of KONECT are (1)~a collection of over 160 network datasets, consisting of directed, undirected, unipartite, bipartite, weighted, unweighted, signed and temporal networks collected from the Web, (2)~a Matlab toolbox for network analysis and (3)~a website giving a compact overview the various computed statistics and plots. In this paper, we describe KONECT's taxonomy of networks datasets, give an overview of the datasets included, review the supported statistics and plots, and briefly discuss KONECT's role in the area of web science and network science.
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KONECTThe Koblenz Network Collection
Jérôme KunegisInstitute for Web Science and Technologies (WeST), University of Koblenz–Landau
With acknowledgments to everyone who has made network datasets available to the public
Jérôme Kunegis KONECT – The Koblenz Network Collection 2
What Is Koblenz?
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My PhD Thesis
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The Trick Is…
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Everything Is a NETWORK !
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Well, Only Almost Everything Is a Network
Communication
Authorship
Friendship
Interaction
Trust
Co-occurrence
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A Network Dataset Is Like a Gummi Bear
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A Network Dataset Is Like a Gummi Bear
Lots of contentto analyse
Evaluate prediction algorithms
Test network models
Test search and recommender
systems
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When You Have Tested One, You Have Tested All ?!
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Or Do You?
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Diversity of Network Datasets
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Overview
Total: 168 datasets
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Network Formats
U • Undirected D • Directed B • Bipartite
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Edge Weight and Multiplicity Types
= • Multiple + • Positive
w > 0
± • Signed
– • Unweighted
w ± 1
* • Rating
★★★★★
** • Multiple Ratings
★★★★★
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Timestamps
t = 1 t = 2 t = 3
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Example Dataset
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Network Comparison
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Network Comparison: Plots
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More Plots
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Download
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RDF
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Matlab Toolbox
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Handbook of Network Analysis
http://konect.uni-koblenz.de/publications
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