CompositeView

CompositeView computes composite scores from formatted network and non-network data to aggregate conceptually similar datasets and support network relevance ranking and large-scale network analysis.

Key Features:

• Python-based processing: Core implementation and data processing are performed in Python.
• Composite score calculation: Calculates composite scores that aggregate multiple related data points into single representative values.
• Formatted input handling: Processes specifically formatted input data to derive composite scores and network metrics.
• Filtering and aggregation: Supports filtering by node values and edge weights and aggregates data according to those filters.
• Dynamic updating: Auto-calculates and updates composite scores in response to changes in filters or aggregated inputs.
• Large-scale network support: Designed to manage and analyze large-scale networks relevant to bioinformatics, knowledge graphs, and social network analysis.
• Cytoscape integration: Integrates with Cytoscape for network visualization workflows.
• Non-network data handling: Applies composite scoring and aggregation methods to non-network datasets such as Human Development Index inputs.
• Benchmarking and stress testing: Includes stress testing to establish performance benchmarks for data scale and visualization scope.
• Comparative capability: Demonstrates dynamic composite-score calculation and update behavior relative to tools including Excel, Tableau, Cytoscape, Neo4j, NodeXL, and Gephi.

Scientific Applications:

• Network relevance ranking: Applied to relevance rankings produced by SemNet 2.0 for knowledge graph relationship analysis.
• Knowledge graphs and graph-based learning: Supports analysis and ranking within knowledge graph and graph-based learning contexts.
• Bioinformatics networks: Used for large-scale network analyses in bioinformatics applications.
• Social network analysis: Applied to social network datasets for aggregated scoring and ranking.
• Human Development Index analysis: Demonstrated on HDI datasets to aggregate and represent composite indicators.
• Cardiovascular epidemiology: Applied to the Framingham cardiovascular study data for composite scoring and relevance ranking.

Methodology:

Processes specifically formatted input data, computes composite scores by aggregating conceptually similar datasets, applies filtering by node values and edge weights, auto-calculates and updates composite scores upon filter or aggregation changes, integrates outputs with Cytoscape, and uses stress testing to define performance benchmarks.