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Visualize networks, graphs, citation maps, and relational data
Passed 18 of 23 test cases with the skill, and 18 of 23 without it.
0 fixed18 passed either way5 still failing0 broke
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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 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)
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
import networkx as nx
def build_citation_network(citations: list[tuple]) -> dict:
"""
Build and analyze a citation network.
test cases passed, vs 18 / 23 without
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