graph_visualizer.py

import networkx as nx
import matplotlib.pyplot as plt

def visualize_graph(adjacency_graph):
    """Visualizes an adjacency graph using networkx and matplotlib."""

    graph = nx.DiGraph()  # Use DiGraph for directed graph

    for node, edges in adjacency_graph.items():
        for edge in edges:
            graph.add_edge(node, edge['other_id'], weight=edge['similarity'])

    pos = nx.spring_layout(graph)  # Node positioning for visualization

    # Get edge weights and normalize them
    weights = [edge['weight'] for u, v, edge in graph.edges(data=True)]
    normalized_weights = [(w - min(weights)) / (max(weights) - min(weights)) for w in weights]

    # Create a color map
    cmap = plt.cm.viridis

    # Map normalized weights to colors
    edge_colors = [cmap(w) for w in normalized_weights]
    
    nx.draw(graph, pos, with_labels=True, node_size=1500, node_color="skyblue", font_size=10, edge_color=edge_colors)
    edge_labels = nx.get_edge_attributes(graph, 'weight')
    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels, font_size=8)


    plt.title("Adjacency Graph")
    plt.show()

if __name__ == "__main__":
    adjacency_graph = {"G3250":[{"other_id":"E2029","similarity":0.8886164931432184},{"other_id":"G4687","similarity":0.7799796722164661},{"other_id":"G2491","similarity":0.7358682896118928}],"E2029":[{"other_id":"G3250","similarity":0.589185898055919},{"other_id":"G4687","similarity":0.5547903202019775},{"other_id":"G2491","similarity":0.4763465778429552}],"G4687":[{"other_id":"G3250","similarity":0.8534847661087587},{"other_id":"E2029","similarity":0.382888810662511},{"other_id":"G2491","similarity":0.9591597530883424}],"G2491":[{"other_id":"G3250","similarity":0.11935305775711214},{"other_id":"E2029","similarity":0.3629634156202275},{"other_id":"G4687","similarity":0.810511185411589}]}
    visualize_graph(adjacency_graph)