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The first scientific data repository with interactive visual analytics
Interactive network visualization and mining of network data sets
Explore network data sets and visualize their structure
Download hundreds of real-world networks: from biological to social networks


Explore network data sets and visualize their structure
Interactive statistics and plots
Download massive network data of billions of edges

Network Repository. An Interactive Scientific Data Repository.

a scientific network data repository with interactive visual analytic tools.

The first interactive data and network repository with real-time analytics. Network repository is not only the first interactive repository, but also the largest network and graph data repository with over 500+ donations. This large comprehensive collection of network graph data is useful for making significant research findings as well as benchmark data sets for a wide variety of applications and domains (e.g., network science, bioinformatics, machine learning, data mining, physics, and social science) and includes relational, attributed, heterogeneous, streaming, spatial, and time series data as well as non-relational machine learning data. All data sets are easily downloaded into a standard consistent format. We also have built a multi-level interactive graph analytics engine that allows users to visualize the structure of the networks as well as macro-level graph statistics as well as important micro-level properties of the nodes and edges.

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Our vision

Scientific progress depends on standard datasets for which claims, hypotheses, and algorithms can be compared and evaluated. Despite the importance of having standard datasets, it is often impossible to find the original data used in published experiments, and at best it is difficult and time consuming. This site is an effort to improve and facilitate the scientific study of networks by making it easier for researchers to download, analyze, and investigate a large collection of network data. Our goal is to make these scientific datasets widely available to everyone while also providing a first attempt at interactive analytics on the web.

We are always looking for talented individuals to help us with this project, so please contact us if you'd like to contribute to this project.

Download network data!

Hundreds of benchmark data sets

Download hundreds of benchmark network data sets from a variety of network types. Also share and contribute by uploading recent network data sets. Naturally all conceivable data may be represented as a graph for analysis. This includes recommendation system data (user purchases products, or user trusts another user), social networks, web graph data, and numerous other real-world datasets.

Networks may be visualized interactively via our web-based network analysis tool.

  • SIGKDD Scientific data repositories have historically made data widely accessible to the scientific community, and have led to better research through comparisons, reproducibility, as well as further discoveries and insights. Despite the growing importance and utilization of data repositories in many scientific disciplines, the design of existing data repositories has not changed for decades. In this paper, we revisit the current design and envision interactive data repositories, which not only make data accessible, but also provide techniques for interactive data exploration, mining, and visualization in an easy, intuitive, and free-flowing manner.

  • Network Repository (NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML Data Repository, and SNAP), the network data repository (networkrepository.com) allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive graph analytics platform. Users can in real-time analyze, visualize, compare, and explore data along many different dimensions. The aim of NR is to make it easy to discover key insights into the data extremely fast with little effort while also providing a medium for users to share data, visualizations, and insights. Other key factors that differentiate NR from the current data repositories is the number of graph datasets, their size, and variety. While other data repositories are static, they also lack a means for users to collaboratively discuss a particular dataset, corrections, or challenges with using the data for certain applications. In contrast, we have incorporated many social and collaborative aspects into NR in hopes of further facilitating scientific research (e.g., users can discuss each graph, post observations, visualizations, etc.).