A Web-based Interactive Visual Graph Analytics Platform

TL;DR

This paper introduces GraphVis, a fast, web-based platform for interactive, real-time graph visualization and analysis, enabling users to explore networks at multiple levels with immediate feedback and novel analytic tools.

Contribution

The paper presents a novel web-based platform combining real-time interactive visualization, multi-level analysis, and new graph analytic techniques for network exploration and understanding.

Findings

Real-time visualization and exploration within seconds

Supports multi-level, dynamic graph analysis with immediate feedback

Includes novel techniques for role discovery and community detection

Abstract

This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision making. Networks can be visualized and explored within seconds by simply drag-and-dropping a graph file into the web browser. The structure, properties, and patterns of the network are computed automatically and can be instantly explored in real-time. At the heart of GraphVis lies a multi-level interactive network visualization and analytics engine that allows for real-time graph mining and exploration across multiple levels of granularity simultaneously. Both the graph analytic and visualization techniques (at each level of granularity) are dynamic and interactive, with immediate and continuous visual feedback upon every user interaction (e.g., change of a slider for filtering). Furthermore, nodes, edges, and subgraphs are easily inserted, deleted or exported via a number of novel techniques and tools that make it extremely easy and flexible for exploring, testing hypothesis, and understanding networks in real-time over the web. A number of interactive visual graph analytic techniques are also proposed including interactive role discovery methods, community detection, as well as a number of novel block models for generating graphs with community structure. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning, as well as tools for dynamic network analysis and visualization, interactive graph generators, and a variety of multi-level network analysis, summarization, and statistical techniques.