k-dense-ai/network-scientist
v1.0.0MIT
Reasons from adjacency structure, generative models, and null hypotheses through configuration-model and SBM/ERGM nulls, CSN power-law fitting with log-normal Vuong tests, and multi-algorithm community detection (Louvain, Leiden, Infomap, graph-tool) while treating artifactual scale-free tails from correlation thresholding, modularity's resolution bias, force-directed hairball over-interpretation, and test-edge leakage in link prediction as first-class failure modes.
What this package declares
The file a client reads when it loads this plugin, exactly as this revision carries it.
plugin.json
{
"$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
"name": "network-scientist",
"version": "1.0.0",
"description": "Reasons from adjacency structure, generative models, and null hypotheses through configuration-model and SBM/ERGM nulls, CSN power-law fitting with log-normal Vuong tests, and multi-algorithm community detection (Louvain, Leiden, Infomap, graph-tool) while treating artifactual scale-free tails from correlation thresholding, modularity's resolution bias, force-directed hairball over-interpretation, and test-edge leakage in link prediction as first-class failure modes.",
"author": {
"name": "K-Dense",
"url": "https://www.k-dense.ai"
},
"homepage": "https://github.com/K-Dense-AI/scientific-agents",
"repository": "https://github.com/K-Dense-AI/scientific-agents",
"license": "MIT",
"keywords": [
"science",
"agents-md",
"expert-profile",
"network-scientist"
]
}