network-visualization-guide

Network Visualization Guide

A skill for visualizing networks, graphs, and relational data in research. Covers NetworkX for analysis, layout algorithms, publication-quality styling, and tools for citation networks, social networks, and knowledge graphs.

Network Basics

When to Use Network Visualization

Network visualization is appropriate when your data involves relationships:
  - Citation networks (papers citing other papers)
  - Co-authorship networks (researchers who collaborate)
  - Social networks (individuals connected by interactions)
  - Biological networks (protein interactions, gene regulation)
  - Knowledge graphs (concepts linked by relationships)
  - Trade/flow networks (countries, organizations, resources)

Key Concepts

Nodes (vertices): The entities in your network
Edges (links):    The relationships between entities
Directed:         Edges have direction (A -> B)
Undirected:       Edges are bidirectional (A -- B)
Weighted:         Edges have a strength or value

Building Networks with NetworkX

Creating and Analyzing a Network

import networkx as nx


def build_citation_network(citations: list[tuple]) -> dict:
    """
    Build and analyze a citation network.

    Args:
        citations: List of (citing_paper, cited_paper) tuples
    """
    G = nx.DiGraph()
    G.add_edges_from(citations)

    metrics = {
        "n_nodes": G.number_of_nodes(),
        "n_edges": G.number_of_edges(),
        "density": nx.density(G),
        "most_cited": sorted(
            G.in_degree(), key=lambda x: x[1], reverse=True
        )[:10],
        "most_citing": sorted(
            G.out_degree(), key=lambda x: x[1], reverse=True
        )[:10],
        "connected_components": nx.number_weakly_connected_components(G)
    }

    # PageRank (importance measure)
    pagerank = nx.pagerank(G)
    metrics["top_pagerank"] = sorted(
        pagerank.items(), key=lambda x: x[1], reverse=True
    )[:10]

    return metrics