Hierarchical Network Visualization Using Clustering Algorithms
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Solution Overview
Problem
Visualizing large communication networks is complex due to thousands of nodes and hundreds of thousands of relationships, making it difficult for network security experts to comprehend and manage network security effectively.
Innovation Solution
A method that simplifies network representation by dividing the graph into layers using clustering iterations based on topological similarities and structural metadata, reducing the complexity and resource requirements for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all network nodes and relationships are visualized in detail, then complete network topology information is provided, but the visualization becomes messy and incomprehensible
Solution Approach 1:
The patent divides the network graph into multiple hierarchical levels or layers, where each level represents a different degree of abstraction. At higher levels, networks are represented as aggregated nodes or clusters, while lower levels provide detailed node-level views. This segmentation allows users to navigate from an overview to specific details without overwhelming visual complexity at any single level.
Solution Approach 2:
The patent introduces a hierarchical dimension to the visualization, transforming a two-dimensional messy graph into a multi-level structure. By adding the vertical dimension of hierarchy (from aggregate to detailed views), the system preserves complete topology information while organizing it in a comprehensible manner through drill-down capabilities.
2Loss of information
If detailed network graphs with thousands of nodes are displayed, then complete network structure is shown, but computational resources and display time increase significantly
Solution Approach 1:
The network visualization is segmented into hierarchical levels where only the necessary level of detail is rendered at any given time. The system displays aggregated views at higher levels and detailed views only when users drill down into specific regions, significantly reducing the number of nodes and edges that need to be processed and rendered simultaneously.
Solution Approach 2:
The system pre-computes and stores hierarchical aggregations of the network data in advance. When a user requests a view at a particular level, the pre-computed aggregated data is quickly retrieved and displayed, avoiding the need to process thousands of individual nodes in real-time and thus improving display speed.
3Device complexity
If hierarchical grouping and aggregation of network nodes is performed, then visualization complexity is reduced, but detail information may be lost
Solution Approach 1:
The hierarchical grouping segments the network into different levels of abstraction. At each level, nodes are aggregated into clusters or super-nodes, simplifying the overall visualization. However, the system preserves the underlying detailed node information and enables users to drill down into any aggregated cluster to view the constituent nodes and their detailed relationships, ensuring no information is permanently lost.
Solution Approach 2:
The patent implements a nested hierarchical structure where aggregated network clusters contain sub-clusters, which in turn contain individual nodes. Each level of aggregation is nested within the previous level, allowing users to progressively drill down from high-level summaries to detailed node information, with each level preserving the structure and data of the levels below it.
Data Source
AI summary
A method that may include receiving network information indicative of (a) network elements that comprise edge network element, (b) connectivity between the network elements, and (c) connectivity of edge network elements to one or more other networks; and generating a visual representation of the network, the visual representation comprises multiple layers, each layer comprises one or more of the network elements of the network, wherein different layers are associated with different importance values; wherein the visual representation is associated with selection metadata for selecting which part out of multiple parts of the visual representation to display, wherein each part comprises at least a part of a single layer the multiple layers.