The first interactive network repository with visual analytic tools
The largest network repository with thousands of network data sets
Interactive network visualization and mining
Download thousands 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
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 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.
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of 94.2% was achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, real-world networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of an arbitrary network. Second, classifying synthetic networks is trivial as our models can easily distinguish between synthetic graphs and the real-world networks they are supposed to model.
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.).