3D Graph Visualization via Community Clustering and Unpacking
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Solution Overview
Problem
Conventional 3D data visualization methods face challenges in efficiently rendering large datasets in 3D virtual spaces, particularly with high cardinality data sets, leading to significant computational requirements and lag, especially when using Virtual Reality (VR) and Augmented Reality (AR) devices.
Innovation Solution
The system employs Markov Clustering and Louvain Modularity to rapidly cluster nodes into supernodes, spatialize the community graph, and then unpack these supernodes into their constituent nodes, utilizing GPU processing to increase computational efficiency and parallelize the spatialization process, allowing for near real-time visualization of large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional 3D data visualization methods are used to render large datasets, then the data can be displayed in 3D virtual spaces, but the computational requirements and processing time increase significantly
Solution Approach 1:
The patent divides the large graph into multiple communities or clusters using clustering algorithms. Each community is processed and visualized separately, reducing the computational burden on any single rendering operation while maintaining the overall structure and relationships in the 3D visualization.
Solution Approach 2:
The patent transitions from 2D graph representations to 3D virtual space visualization. By utilizing the third dimension, the system can accommodate and display much larger datasets with millions of nodes without increasing processing time proportionally, as the spatial distribution in 3D allows for more efficient rendering and data organization.
2Quantity of substance
If conventional 3D data visualization methods are used to render large datasets, then the data can be displayed in 3D virtual spaces, but the computational complexity and processing power requirements increase significantly
Solution Approach 1:
The patent segments the large graph into smaller community subgraphs that can be processed independently. This segmentation reduces the computational complexity of graph operations such as clustering, layout calculation, and rendering, as each subgraph requires fewer computational resources than the complete graph.
Solution Approach 2:
The patent performs preliminary clustering and community detection operations before the main visualization process. By pre-processing the graph to identify communities and their structures, the system reduces the computational complexity of subsequent rendering and interaction operations, as the community structure provides a simplified framework for visualization.
3Loss of information
If conventional graph clustering methods are used, then communities can be identified in the graph, but the computational time and resources required are excessive for large datasets
Solution Approach 1:
The patent applies segmentation by dividing the large graph into smaller subgraphs or processing chunks. Community detection algorithms are applied to these smaller segments rather than the complete graph, significantly reducing computation time while preserving the community structure within each segment. The segments are then integrated to form the complete community structure.
Solution Approach 2:
The patent performs preliminary filtering and preprocessing of the graph data before applying community detection algorithms. This preliminary action includes removing redundant edges, filtering low-weight connections, and organizing the graph data into an optimized structure, which reduces the computational time required for clustering while maintaining the accuracy of community identification.
Data Source
AI summary
Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.