graph: community isolation scoring + sort:isolation query
Add community_isolation() to Graph — computes per-community ratio of internal vs total edge weight. 1.0 = fully isolated, 0.0 = all edges external. New query: sort:isolation — sorts nodes by their community's isolation score, most isolated first. Useful for aiming organize agents at poorly-integrated knowledge clusters. New CLI: poc-memory graph communities [N] [--min-size M] — lists communities sorted by isolation with member preview. Reveals islands like the Shannon theory cluster (3 nodes, 100% isolated, 0 cross-edges) and large agent-journal clusters (20-30 nodes, 95% isolated). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@ -656,3 +656,40 @@ pub fn cmd_interference(threshold: f32) -> Result<(), String> {
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Ok(())
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}
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/// Show communities sorted by isolation (most isolated first).
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/// Useful for finding poorly-integrated knowledge clusters that need
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/// organize agents aimed at them.
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pub fn cmd_communities(top_n: usize, min_size: usize) -> Result<(), String> {
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let store = store::Store::load()?;
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let g = store.build_graph();
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let infos = g.community_info();
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let total = infos.len();
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let shown: Vec<_> = infos.into_iter()
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.filter(|c| c.size >= min_size)
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.take(top_n)
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.collect();
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println!("{} communities total ({} with size >= {})\n",
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total, shown.len(), min_size);
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println!("{:<6} {:>5} {:>7} {:>7} members", "id", "size", "iso", "cross");
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println!("{}", "-".repeat(70));
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for c in &shown {
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let preview: Vec<&str> = c.members.iter()
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.take(5)
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.map(|s| s.as_str())
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.collect();
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let more = if c.size > 5 {
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format!(" +{}", c.size - 5)
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} else {
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String::new()
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};
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println!("{:<6} {:>5} {:>6.0}% {:>7} {}{}",
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c.id, c.size, c.isolation * 100.0, c.cross_edges,
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preview.join(", "), more);
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}
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Ok(())
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}
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