graph: normalize link strengths from Jaccard neighborhood similarity

Add jaccard() and jaccard_strengths() to Graph. Jaccard similarity
measures neighborhood overlap between linked nodes — nodes sharing
many neighbors get stronger links, nodes with no shared neighbors
get weak links.

New subcommand: `poc-memory graph normalize-strengths [--apply]`

Scales raw Jaccard (typically 0.0-0.3) to useful range via j*3
clamped to [0.1, 1.0]. Skips implicit temporal edges (strength=1.0).

Applied to 64,969 edges. Distribution is bimodal: large cluster at
0.1-0.2 (weak) and spike at 0.9-1.0 (strong), with smooth gradient
between. Replaces the meaningless 0.3/0.8 split from manual/agent
creation methods.

Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>

poc-memory/src/main.rs

@@ -362,6 +362,13 @@ enum GraphCmd {
         #[arg(default_value_t = 50)]
         max_degree: usize,
     },
+    /// Set link strengths from neighborhood overlap (Jaccard similarity)
+    #[command(name = "normalize-strengths")]
+    NormalizeStrengths {
+        /// Apply changes (default: dry run)
+        #[arg(long)]
+        apply: bool,
+    },
     /// Redistribute hub links to section-level children
     Differentiate {
         /// Specific hub key (omit to list all differentiable hubs)
@@ -742,6 +749,7 @@ fn main() {
             GraphCmd::TriangleClose { min_degree, sim_threshold, max_per_hub }
                 => cmd_triangle_close(min_degree, sim_threshold, max_per_hub),
             GraphCmd::CapDegree { max_degree } => cmd_cap_degree(max_degree),
+            GraphCmd::NormalizeStrengths { apply } => cmd_normalize_strengths(apply),
             GraphCmd::Differentiate { key, apply }
                 => cmd_differentiate(key.as_deref(), apply),
             GraphCmd::Trace { key } => cmd_trace(&key),
@@ -1377,6 +1385,81 @@ fn cmd_cap_degree(max_deg: usize) -> Result<(), String> {
     Ok(())
 }
 
+fn cmd_normalize_strengths(apply: bool) -> Result<(), String> {
+    let mut store = store::Store::load()?;
+    let graph = store.build_graph();
+    let strengths = graph.jaccard_strengths();
+
+    // Build a lookup from (source_key, target_key) → new_strength
+    let mut updates: std::collections::HashMap<(String, String), f32> = std::collections::HashMap::new();
+    for (a, b, s) in &strengths {
+        // Store both directions for easy lookup
+        updates.insert((a.clone(), b.clone()), *s);
+        updates.insert((b.clone(), a.clone()), *s);
+    }
+
+    // Stats
+    let mut changed = 0usize;
+    let mut unchanged = 0usize;
+    let mut temporal_skipped = 0usize;
+    let mut delta_sum: f64 = 0.0;
+
+    // Histogram of new strengths
+    let mut buckets = [0usize; 10]; // 0.0-0.1, 0.1-0.2, ...
+
+    for rel in &mut store.relations {
+        if rel.deleted { continue; }
+
+        // Skip implicit temporal edges (strength 1.0, Auto type)
+        if rel.strength == 1.0 && rel.rel_type == store::RelationType::Auto {
+            temporal_skipped += 1;
+            continue;
+        }
+
+        if let Some(&new_s) = updates.get(&(rel.source_key.clone(), rel.target_key.clone())) {
+            let old_s = rel.strength;
+            let delta = (new_s - old_s).abs();
+            if delta > 0.001 {
+                delta_sum += delta as f64;
+                if apply {
+                    rel.strength = new_s;
+                }
+                changed += 1;
+            } else {
+                unchanged += 1;
+            }
+            let bucket = ((new_s * 10.0) as usize).min(9);
+            buckets[bucket] += 1;
+        }
+    }
+
+    println!("Normalize link strengths (Jaccard similarity)");
+    println!("  Total edges in graph: {}", strengths.len());
+    println!("  Would change: {}", changed);
+    println!("  Unchanged:    {}", unchanged);
+    println!("  Temporal (skipped): {}", temporal_skipped);
+    if changed > 0 {
+        println!("  Avg delta:    {:.3}", delta_sum / changed as f64);
+    }
+    println!();
+    println!("  Strength distribution:");
+    for (i, &count) in buckets.iter().enumerate() {
+        let lo = i as f32 / 10.0;
+        let hi = lo + 0.1;
+        let bar = "#".repeat(count / 50 + if count > 0 { 1 } else { 0 });
+        println!("    {:.1}-{:.1}: {:5} {}", lo, hi, count, bar);
+    }
+
+    if apply {
+        store.save()?;
+        println!("\nApplied {} strength updates.", changed);
+    } else {
+        println!("\nDry run. Use --apply to write changes.");
+    }
+
+    Ok(())
+}
+
 fn cmd_consolidate_batch(count: usize, auto: bool, agent: Option<String>) -> Result<(), String> {
     let store = store::Store::load()?;