Add KER Directionality Network Metrics

[marvinm2]

Description

Analyze upstream-to-downstream Key Event Relationship patterns to identify branching points, convergence nodes, and linear chains in the AOP network. This shows network topology characteristics and helps understand pathway structure beyond simple entity counts.

Visualization Types

• Latest snapshot: Current network topology metrics

Value & Priority

• Priority: Phase 3 - Lower Priority
• Value: Medium (advanced network analysis)
• Complexity: Medium

Implementation Details

Key Data Requirements

Properties from property_labels.csv:

• Upstream Key Event (has_upstream_key_event) - KER
• Downstream Key Event (has_downstream_key_event) - KER

Calculate graph metrics:

• In-degree distribution: How many upstream KEs each KE has
• Out-degree distribution: How many downstream KEs each KE has
• Convergence nodes: KEs with multiple upstream connections
• Divergence nodes: KEs with multiple downstream connections
• Linear chains: Sequential KEs with single in/out connections

Visualization Format

• Degree distribution histogram: Show in-degree and out-degree patterns
• Node categorization: Pie chart showing convergence/divergence/linear node types
• Network topology summary: Key metrics (avg degree, max degree, etc.)
• Bottleneck identification: Highlight high-degree KEs

Expected Insights

• Understand AOP network structure beyond entity counts
• Identify "hub" KEs that are convergence or divergence points
• Reveal linear vs. branched pathway patterns
• Support network analysis and systems biology modeling
• Guide pathway integration efforts

Performance Notes

• Medium complexity graph analysis
• Requires building network representation in Python
• May be expensive for large networks
• Consider using NetworkX or similar library