benchmark_layouts.py

"""
Visualization Example: Layout Computation Benchmark

This example demonstrates how to benchmark different layout algorithms
for network visualization, particularly comparing:
1. Traditional force-directed layouts
2. Embedding-based layouts (using node2vec + dimensionality reduction)

Key Idea: Compute layout by first computing node embeddings, then
projecting them to 2D space using t-SNE or similar techniques.

This approach can be faster and produce better layouts for large networks
compared to traditional force-directed algorithms.

Note: This is a benchmark/comparison template. The actual implementation
would require adding specific benchmark code based on your needs.

Runtime: FAST (< 5 seconds) - Standalone example suitable for CI
"""

import sys

print("=" * 70)
print("LAYOUT ALGORITHM BENCHMARK")
print("=" * 70)

print("\nThis example is a template for benchmarking layout algorithms.")
print("\nTypical approaches to compare:")
print("  1. Force-directed layouts (e.g., ForceAtlas2, Fruchterman-Reingold)")
print("  2. Embedding-based layouts (e.g., Node2Vec + t-SNE)")
print("  3. Spectral layouts")
print("  4. Multilayer-specific layouts")

print("\nKey considerations for layout benchmarks:")
print("  - Runtime performance (seconds/iterations)")
print("  - Visual quality (edge crossings, node overlap)")
print("  - Scalability (performance with network size)")
print("  - Preservation of community structure")
print("  - Layout stability across runs")

print("\nSuggested workflow:")
print("  1. Generate test networks of varying sizes")
print("  2. Apply each layout algorithm")
print("  3. Measure computation time")
print("  4. Evaluate visual quality metrics")
print("  5. Compare results")

print("\n" + "=" * 70)
print("To implement a full benchmark, add code to:")
print("  - Load or generate test networks")
print("  - Apply different layout algorithms")
print("  - Time each algorithm")
print("  - Visualize and compare results")
print("=" * 70)

# Example benchmark structure (pseudo-code):
print("\nExample benchmark structure:")
print("""
from py3plex.core import multinet, random_generators
from py3plex.visualization.multilayer import hairball_plot
import time

# Generate test network
network = random_generators.random_multilayer_ER(1000, 5, 0.01, directed=False)

# Benchmark 1: Force-directed layout
start = time.time()
hairball_plot(network.core_network, layout_algorithm="force",
              layout_parameters={"iterations": 100}, show=False)
force_time = time.time() - start
print(f"Force-directed: {force_time:.2f}s")

# Benchmark 2: Embedding-based layout
# (Requires node2vec and t-SNE setup)
# start = time.time()
# ... embedding computation ...
# embedding_time = time.time() - start
# print(f"Embedding-based: {embedding_time:.2f}s")

# Compare quality metrics
# ... compute edge crossings, node overlap, etc. ...
""")