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NETWORK REPOSITORY
A SCIENTIFIC NETWORK DATA REPOSITORY WITH
INTERACTIVE VISUALIZATION and MINING TOOLS

The first interactive network repository with visual analytic tools
The largest network data repository with thousands of network data sets
Interactive network visualization and mining
Download thousands of real-world network datasets: from biological to social networks

NETWORK DATA SETS
INTERACTIVE VISUALIZATION
DOWNLOAD NETWORK DATA SETS

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 Network Data Repository.

the first scientific network data repository with interactive visual analytics.

new GraphVis: interactive visual graph mining and machine learning英国365bet官方

The first interactive data and network data repository with real-time visual analytics. Network repository is not only the first interactive repository, but also the largest network repository with thousands of donations in 30+ domains (from biological to social network data). This large comprehensive collection of network graph data is useful for making significant research findings as well as benchmark network 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 network data as well as non-relational machine learning data. All graph 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 network data as well as macro-level graph data statistics as well as important micro-level network properties of the nodes and edges.
Check out GraphVis: the interactive visual network mining and machine learning tool.



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About

Our vision

Scientific progress depends on standard graph datasets for which claims, hypotheses, and algorithms can be compared and evaluated. Despite the importance of having standard network 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 graph 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 network 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 social network data, brain networks, temporal network data, web graph datasets, road networks, retweet networks, labeled graphs, and numerous other real-world graph datasets.

Network data can be visualized and explored in real-time on the web via our web-based interactive network visual analytics platform.




  • Try the new interactive visual graph data mining and machine learning platform! This is a free demo version of . It can be used to analyze and explore network data in real-time over the web. GraphVis is also extremely useful as an educational tool as it allows an individual to interactively explore and understand fundamental key concepts in graph theory, network science, and machine learning. For more details, use cases, and ways of using and combining these interactive tools and functionality, see and the .

    The platform combines interactive visual representations with state-of-the-art network data mining and relational machine learning techniques to aid in revealing important insights quickly in real-time over the web. Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques. 英国365bet官方 Most graph data formats are supported (edge lists, mtx, gml, xml, graphml, json, paj, net, etc.), simply drag and drop your network dataset into the browser window (or load one from network data repository using the left menu). For a demo of some features, see and .

       interactive visual graph mining


  • 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 (garlandal.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.).


NSF Funded Research