[Paper Review] Handbook of Network Analysis [KONECT -- the Koblenz Network Collection]

Jérôme Kunegis|arXiv (Cornell University)|Feb 22, 2014

Complex Network Analysis Techniques38 references26 citations

TL;DR

This handbook introduces KONECT (the Koblenz Network Collection), a comprehensive, open-access repository of 214+ network datasets spanning diverse domains such as social networks, web graphs, and collaboration systems. It standardizes network analysis through a unified taxonomy, consistent metadata tagging, and integrated Matlab tooling, enabling reproducible, cross-disciplinary network science research with standardized statistics, visualizations, and file formats.

ABSTRACT

This is the handbook for the KONECT project, the \emph{Koblenz Network Collection}, a scientific project to collect, analyse, and provide network datasets for researchers in all related fields of research, by the Namur Center for Complex Systems (naXys) at the University of Namur, Belgium, with web hosting provided by the Institute for Web Science and Technologies (WeST) at the University of Koblenz--Landau, Germany.

Motivation & Objective

• To address the lack of standardized, comparable network datasets across network science research by creating a unified, accessible repository.
• To enable cross-disciplinary network analysis by defining a comprehensive taxonomy and consistent metadata tagging system for diverse network types.
• To support reproducible research by providing standardized statistics, visualizations, and Matlab-based analysis tooling for all datasets.
• To facilitate the integration of network data from disparate sources—such as social media, citation networks, and web graphs—into a single, consistent framework.
• To improve data quality and usability by tagging datasets with provenance, completeness, and structural properties (e.g., #incomplete, #lcc, #tournament).

Proposed method

• KONECT organizes network datasets into a standardized taxonomy based on format (undirected, directed, bipartite), edge weight types, multiplicities, and metadata (e.g., timestamps, labels).
• Each network is assigned a unique two- or three-character code and is tagged with metadata flags (e.g., #incomplete, #lcc, #tournament) to indicate structural and data quality properties.
• The project provides a Matlab toolbox for computing network statistics (e.g., degree distribution, clustering coefficient) and generating visualizations (e.g., scatter plots of nodes vs. average degree).
• KONECT supports multiple file formats, including plain text, edge lists, and RDF-compliant N3 format, with extensible metadata fields for node and edge data.
• The system includes a web interface (konect.uni-koblenz.de) and GitHub-hosted codebases (e.g., konect-toolbox, konect-handbook) for public access and reproducibility.
• Networks are extracted and validated using automated pipelines, with support for regeneration and updates via the #regenerate tag.

Experimental results

Research questions

• RQ1 How can network science research be made more reproducible and comparable across different datasets and domains?
• RQ2 What standardized metadata and tagging system can improve data quality and interoperability across diverse network datasets?
• RQ3 How can a unified framework for network analysis support cross-disciplinary research in social networks, web science, and machine learning?
• RQ4 What are the key structural and statistical properties of real-world networks across different application domains?
• RQ5 How can network datasets be consistently represented, stored, and visualized to enable large-scale analysis and tool interoperability?

Key findings

• KONECT hosts 214 network datasets as of October 2014, ranging from small classical datasets (e.g., Highland Tribes with 16 nodes) to massive real-world networks like the Twitter social network (52M nodes, 1.9B edges).
• The project provides a consistent, standardized taxonomy and metadata tagging system (e.g., #incomplete, #lcc, #tournament) that enables reliable comparison and analysis across datasets.
• The KONECT Matlab toolbox enables automated computation of key network statistics and generation of visualizations, such as scatter plots of network size versus average degree.
• The system supports multiple data formats, including edge lists, RDF/N3, and structured metadata, with extensible fields for node and edge attributes.
• KONECT’s web platform and GitHub-hosted codebases (e.g., konect-toolbox, konect-handbook) ensure long-term accessibility, reproducibility, and community contribution.
• The project has been sustained through European Union funding (e.g., ROBUST, SocialSensor, REVEAL) and is hosted by the University of Koblenz–Landau with development ongoing at the University of Namur.