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A Method and System for Visualizing, Exploring, and Analyzing Huge Graph Based on Query

IP.com Disclosure Number: IPCOM000226964D
Publication Date: 2013-Apr-26
Document File: 3 page(s) / 87K

Publishing Venue

The IP.com Prior Art Database

Abstract

This invention is about a multilevel decomposition based approach for huge graph visualization

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

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A Method and System for Visualizing, Exploring, and Analyzing Huge Graph Based on Query

Traditional graph visualizations usually contains two types of design, the node link graphs and the matrix visualization. The matrix visualization represents a adjacency matrix of the graph. Some researchers combined these two types of visualization together generated a visualization called NodeTrix. All these visualizations target on visualizing relational pattern of the graphs. Even today, this guideline is still been widely used. Many elegant visualizations has been designed to show the overview of an entire dataset. The extreme goal of showing the entire dataset is to squeezing millions and even billions of records into million pixels. To visualizing a graph with the above complex information, we are facing three major challenges : 1). How to reduce visual clutters ? 2). Performance issues, 3). How can users understand the visualization results since their cognition is limited.

The above figure illustrates the basic idea of our procedure, which includes three steps (1)Graph Decomposition; (2) Link Estimation; (3) Interactive Visualization

The business value of this method is tremendous, it can be used in any cases that need to represent, navigate, and analysis a huge graph, including (1) Social network analysis (System-G, SmallBlue, Lotus Connection): represent the social structure at the enterprise level; (2) Healthcare (patient care & insights): patient networks to detect clinically similar patients (patient cohorts); (3) Data Analysis (SPSS): Help on explore and navigate in the huge node-link structures like Bayesian networks.

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The above figure includes the whole procedure of the disclosure, where the main steps are:

-Decomposing graph data as community components that are connected by bridge components

-Estimating the strength of connections between different com...