Proceedings of the AAAI Conference on Artificial Intelligence· 2015
The Network Data Repository with Interactive Graph Analytics and Visualization
- 2,430citations
- 2015year
Short summary
NetworkRepository (NR) is a novel web-based platform that allows users to download, interactively analyze, and visualize network data in real-time, differentiating itself from static repositories.
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Key points
- Introduces NetworkRepository (NR), an interactive web-based platform for network data.
- Allows real-time interactive analysis and visualization of graph data.
- Differentiates from static repositories by offering dynamic analysis and collaborative features.
- Includes social aspects like discussions and sharing of observations and visualizations.
AI-generated from the title and abstract; the full text is not read.
Abstract
NetworkRepository (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, NR incorporates many social and collaborative aspects that facilitate scientific research, e.g., users can discuss each graph, post observations, and visualizations.
The authors' abstract, as published at the source. Proceedings of the AAAI Conference on Artificial Intelligence, 2015 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy