Agent skill · vasilyu1983
foundations-network-science
Network-science primitives for graph systems, centrality, PageRank, communities, contagion, link prediction, and temporal networks. Use when analyzing graph structure.
What it needs
About 11k tokens when loaded.
What this skill does
Network Science Foundations 12 canonical network-science primitives, each solving a distinct structural or dynamic analysis problem. Primitives are domain-agnostic: the same PageRank that ranks web pages ranks citation authority, package influence, and audience amplification. The same percolation threshold that governs epidemic spread governs cascading failure in dependency graphs. When to Apply Apply network-science when: The data IS a graph — citations, dependencies, follower graphs, supply chains, knowledge graphs The system is a graph even if the data is not — LLM multi-agent communication topology, agent memory graphs, tool-call graphs (see Agent Topology as a Graph Problem) Spread/contagion question — viral coefficient, R₀, percolation threshold Centrality question — "which nodes are critical?" (PageRank, betweenness, eigenvector) Community detection — clustering nodes by structural similarity (Louvain, Leiden) Blast-radius / dependency-impact analysis on services or modules Skip and use simpler alternatives when: Data is tabular and relationships aren't structural — standard analytics suffices Graph has < 50 nodes — visual inspection beats algorithmic centrality Question is about strategic interaction at the node level — use foundations-game-theory Question is about queue or flow through a single bottleneck — use foundations-queueing-theory or theory-of-constraints Edges are weak proxies (e.g. "users who viewed both products") — centrality is unreliable; validate edge semantics first "Network effects" is a marketing claim, not a measured viral coefficient — quantify R first or skip the analysis The data is tabular but might still be a graph problem. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills vasilyu1983/foundations-network-science