#!/usr/bin/env python3
"""
Visualize PyTorch Geometric graph structures using networkx and matplotlib.
This script provides utilities to visualize Data objects, including:
- Graph structure (nodes and edges)
- Node features (as colors)
- Edge attributes (as edge colors/widths)
- Community/cluster assignments
Usage:
python visualize_graph.py --dataset Cora --output graph.png
Or import and use:
from scripts.visualize_graph import visualize_data
visualize_data(data, title="My Graph", show_labels=True)
"""
import argparse
import matplotlib.pyplot as plt
import networkx as nx
import torch
from typing import Optional, Union
import numpy as np
def visualize_data(
data,
title: str = "Graph Visualization",
node_color_attr: Optional[str] = None,
edge_color_attr: Optional[str] = None,
show_labels: bool = False,
node_size: int = 300,
figsize: tuple = (12, 10),
layout: str = "spring",
output_path: Optional[str] = None,
max_nodes: Optional[int] = None,
):
"""
Visualize a PyTorch Geometric Data object.
Args:
data: PyTorch Geometric Data object
title: Plot title
node_color_attr: Data attribute to use for node colors (e.g., 'y', 'train_mask')
edge_color_attr: Data attribute to use for edge colors
show_labels: Whether to show node labels
node_size: Size of nodes in visualization
figsize: Figure size (width, height)
layout: Graph layout algorithm ('spring', 'circular', 'kamada_kawai', 'spectral')
output_path: Path to save figure (if None, displays interactively)
max_nodes: Maximum number of nodes to visualize (samples if exceeded)
"""
# Sample nodes if graph is too large
if max_nodes and data.num_nodes > max_nodes:
print(f"Graph has {data.num_nodes} nodes. Sampling {max_nodes} nodes for visualization.")
node_indices = torch.randperm(data.num_nodes)[:max_nodes]
data = data.subgraph(node_indices)
# Convert to networkx graph
G = nx.Graph() if is_undirected(data.edge_index) else nx.DiGraph()
# Add nodes
G.add_nodes_from(range(data.num_nodes))
# Add edges
edge_index = data.edge_index.cpu().numpy()
edges = list(zip(edge_index[0], edge_index[1]))
G.add_edges_from(edges)
# Setup figure
fig, ax = plt.subplots(figsize=figsize)
# Choose layout
if layout == "spring":
pos = nx.spring_layout(G, k=0.5, iterations=50)
elif layout == "circular":
pos = nx.circular_layout(G)
elif layout == "kamada_kawai":
pos = nx.kamada_kawai_layout(G)
elif layout == "spectral":
pos = nx.spectral_layout(G)
else:
raise ValueError(f"Unknown layout: {layout}")
# Determine node colors
if node_color_attr and hasattr(data, node_color_attr):
node_colors = getattr(data, node_color_attr).cpu().numpy()
if node_colors.dtype == bool:
node_colors = node_colors.astype(int)
if len(node_colors.shape) > 1:
# Multi-dimensional features - use first dimension
node_colors = node_colors[:, 0]
else:
node_colors = 'skyblue'
# Determine edge colors
if edge_color_attr and hasattr(data, edge_color_attr):
edge_colors = getattr(data, edge_color_attr).cpu().numpy()
if len(edge_colors.shape) > 1:
edge_colors = edge_colors[:, 0]
else:
edge_colors = 'gray'
# Draw graph
nx.draw_networkx_nodes(
G, pos,
node_color=node_colors,
node_size=node_size,
cmap=plt.cm.viridis,
ax=ax
)
nx.draw_networkx_edges(
G, pos,
edge_color=edge_colors,
alpha=0.3,
arrows=isinstance(G, nx.DiGraph),
arrowsize=10,
ax=ax
)
if show_labels:
nx.draw_networkx_labels(G, pos, font_size=8, ax=ax)
ax.set_title(title, fontsize=16, fontweight='bold')
ax.axis('off')
# Add colorbar if using numeric node colors
if node_color_attr and isinstance(node_colors, np.ndarray):
sm = plt.cm.ScalarMappable(
cmap=plt.cm.viridis,
norm=plt.Normalize(vmin=node_colors.min(), vmax=node_colors.max())
)
sm.set_array([])
cbar = plt.colorbar(sm, ax=ax, fraction=0.046, pad=0.04)
cbar.set_label(node_color_attr, rotation=270, labelpad=20)
plt.tight_layout()
if output_path:
plt.savefig(output_path, dpi=300, bbox_inches='tight')
print(f"Figure saved to {output_path}")
else:
plt.show()
plt.close()
def is_undirected(edge_index):
"""Check if graph is undirected."""
row, col = edge_index
num_edges = edge_index.size(1)
# Create a set of edges and reverse edges
edges = set(zip(row.tolist(), col.tolist()))
reverse_edges = set(zip(col.tolist(), row.tolist()))
# Check if all edges have their reverse
return edges == reverse_edges
def plot_degree_distribution(data, output_path: Optional[str] = None):
"""Plot the degree distribution of the graph."""
from torch_geometric.utils import degree
row, col = data.edge_index
deg = degree(col, data.num_nodes).cpu().numpy()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
# Histogram
ax1.hist(deg, bins=50, edgecolor='black', alpha=0.7)
ax1.set_xlabel('Degree', fontsize=12)
ax1.set_ylabel('Frequency', fontsize=12)
ax1.set_title('Degree Distribution', fontsize=14, fontweight='bold')
ax1.grid(alpha=0.3)
# Log-log plot
unique_degrees, counts = np.unique(deg, return_counts=True)
ax2.loglog(unique_degrees, counts, 'o-', alpha=0.7)
ax2.set_xlabel('Degree (log scale)', fontsize=12)
ax2.set_ylabel('Frequency (log scale)', fontsize=12)
ax2.set_title('Degree Distribution (Log-Log)', fontsize=14, fontweight='bold')
ax2.grid(alpha=0.3)
plt.tight_layout()
if output_path:
plt.savefig(output_path, dpi=300, bbox_inches='tight')
print(f"Degree distribution saved to {output_path}")
else:
plt.show()
plt.close()
def plot_graph_statistics(data, output_path: Optional[str] = None):
"""Plot various graph statistics."""
from torch_geometric.utils import degree, contains_self_loops, is_undirected as check_undirected
# Compute statistics
row, col = data.edge_index
deg = degree(col, data.num_nodes).cpu().numpy()
stats = {
'Nodes': data.num_nodes,
'Edges': data.num_edges,
'Avg Degree': deg.mean(),
'Max Degree': deg.max(),
'Self-loops': contains_self_loops(data.edge_index),
'Undirected': check_undirected(data.edge_index),
}
if hasattr(data, 'num_node_features'):
stats['Node Features'] = data.num_node_features
if hasattr(data, 'num_edge_features') and data.edge_attr is not None:
stats['Edge Features'] = data.num_edge_features
if hasattr(data, 'y'):
if data.y.dim() == 1:
stats['Classes'] = int(data.y.max().item()) + 1
# Create text plot
fig, ax = plt.subplots(figsize=(8, 6))
ax.axis('off')
text = "Graph Statistics\n" + "=" * 40 + "\n\n"
for key, value in stats.items():
text += f"{key:20s}: {value}\n"
ax.text(0.1, 0.5, text, fontsize=14, family='monospace',
verticalalignment='center', transform=ax.transAxes)
plt.tight_layout()
if output_path:
plt.savefig(output_path, dpi=300, bbox_inches='tight')
print(f"Statistics saved to {output_path}")
else:
plt.show()
plt.close()
# Print to console as well
print("\n" + text)
def main():
parser = argparse.ArgumentParser(description="Visualize PyTorch Geometric graphs")
parser.add_argument('--dataset', type=str, default='Cora',
help='Dataset name (e.g., Cora, CiteSeer, ENZYMES)')
parser.add_argument('--output', type=str, default=None,
help='Output file path for visualization')
parser.add_argument('--node-color', type=str, default='y',
help='Attribute to use for node colors')
parser.add_argument('--layout', type=str, default='spring',
choices=['spring', 'circular', 'kamada_kawai', 'spectral'],
help='Graph layout algorithm')
parser.add_argument('--show-labels', action='store_true',
help='Show node labels')
parser.add_argument('--max-nodes', type=int, default=500,
help='Maximum nodes to visualize')
parser.add_argument('--stats', action='store_true',
help='Show graph statistics')
parser.add_argument('--degree', action='store_true',
help='Show degree distribution')
args = parser.parse_args()
# Load dataset
print(f"Loading dataset: {args.dataset}")
try:
# Try Planetoid datasets
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root=f'/tmp/{args.dataset}', name=args.dataset)
data = dataset[0]
except:
try:
# Try TUDataset
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root=f'/tmp/{args.dataset}', name=args.dataset)
data = dataset[0]
except Exception as e:
print(f"Error loading dataset: {e}")
print("Supported datasets: Cora, CiteSeer, PubMed, ENZYMES, PROTEINS, etc.")
return
print(f"Loaded {args.dataset}: {data.num_nodes} nodes, {data.num_edges} edges")
# Generate visualizations
if args.stats:
stats_output = args.output.replace('.png', '_stats.png') if args.output else None
plot_graph_statistics(data, stats_output)
if args.degree:
degree_output = args.output.replace('.png', '_degree.png') if args.output else None
plot_degree_distribution(data, degree_output)
# Main visualization
visualize_data(
data,
title=f"{args.dataset} Graph",
node_color_attr=args.node_color,
show_labels=args.show_labels,
layout=args.layout,
output_path=args.output,
max_nodes=args.max_nodes
)
if __name__ == '__main__':
main()