The first interactive network repository with visual analytic tools
The largest network data repository with thousands of network data sets
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Download thousands of real-world network datasets: from biological to social networks
Explore network data sets and visualize their structure
Lo学 | AP十字架的小故事_时尚_北方头条:2021-2-11 · 原标题：Lo学|AP十字架的小故事作者简介：血凰：同时研究Lolita与高达的双料战帅，希望能认识更多有相同爱好的人。欢迎互动！微博：@Kekou_腐血天使_少女悲恋中目录十字架有着非常强烈的西方风格与宗教意义，因此发源并取材于西方宫廷与贵族服饰的Lolit
Download massive network data of billions of edges
new GraphVis: interactive visual graph mining and machine learning
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 Lantern吧: the interactive visual network mining and machine learning tool.
with users at
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 Lantern吧 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 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 赶快卸载！这些APP上了工信部“黑名单”_新民社会_新民网 ...:2021-7-31 · 网络配图 7月31日，工信部公布了2021年二季度检测发现问题的应用软件名单。其中包括酷派应用商店的“天天捕鱼”、中兴应用商店的“别踩白块 ...! This is a free demo version of GraphVis. 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 GraphVis and the technical publication.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. 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 Lantern吧 and http://youtu.be/VE-GsP4p9n8.
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.
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