Add support for NetworkX for creating, analyzing, and visualizing complex networks and graphs.

docs/scientific-packages.md

@@ -63,6 +63,7 @@
 - **Dask** - Parallel computing for larger-than-memory datasets with distributed DataFrames, Arrays, Bags, and Futures
 - **Data Commons** - Programmatic access to public statistical data from global sources including census bureaus, health organizations, and environmental agencies. Provides unified Python API for querying demographic data, economic indicators, health statistics, and environmental datasets through a knowledge graph interface. Features three main endpoints: Observation (statistical time-series queries for population, GDP, unemployment rates, disease prevalence), Node (knowledge graph exploration for entity relationships and hierarchies), and Resolve (entity identification from names, coordinates, or Wikidata IDs). Seamless Pandas integration for DataFrames, relation expressions for hierarchical queries, data source filtering for consistency, and support for custom Data Commons instances
 - **Matplotlib** - Publication-quality plotting and visualization
+- **NetworkX** - Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs. Supports four graph types (Graph, DiGraph, MultiGraph, MultiDiGraph) with nodes as any hashable objects and rich edge attributes. Provides 100+ algorithms including shortest paths (Dijkstra, Bellman-Ford, A*), centrality measures (degree, betweenness, closeness, eigenvector, PageRank), clustering (coefficients, triangles, transitivity), community detection (modularity-based, label propagation, Girvan-Newman), connectivity analysis (components, cuts, flows), tree algorithms (MST, spanning trees), matching, graph coloring, isomorphism, and traversal (DFS, BFS). Includes 50+ graph generators for classic (complete, cycle, wheel), random (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, stochastic block model), lattice (grid, hexagonal, hypercube), and specialized networks. Supports I/O across formats (edge lists, GraphML, GML, JSON, Pajek, GEXF, DOT) with Pandas/NumPy/SciPy integration. Visualization capabilities include 8+ layout algorithms (spring/force-directed, circular, spectral, Kamada-Kawai), customizable node/edge appearance, interactive visualizations with Plotly/PyVis, and publication-quality figure generation. Use cases: social network analysis, biological networks (protein-protein interactions, gene regulatory networks, metabolic pathways), transportation systems, citation networks, knowledge graphs, web structure analysis, infrastructure networks, and any domain involving pairwise relationships requiring structural analysis or graph-based modeling
 - **Polars** - High-performance DataFrame operations with lazy evaluation
 - **Seaborn** - Statistical data visualization with dataset-oriented interface, automatic confidence intervals, publication-quality themes, colorblind-safe palettes, and comprehensive support for exploratory analysis, distribution comparisons, correlation matrices, regression plots, and multi-panel figures
 - **SimPy** - Process-based discrete-event simulation framework for modeling systems with processes, queues, and resource contention (manufacturing, service operations, network traffic, logistics). Supports generator-based process definition, multiple resource types (Resource, PriorityResource, PreemptiveResource, Container, Store), event-driven scheduling, process interaction mechanisms (signaling, interruption, parallel/sequential execution), real-time simulation synchronized with wall-clock time, and comprehensive monitoring capabilities for utilization, wait times, and queue statistics