diff --git a/src/graph.rs b/src/graph.rs
index cba9b27..1f25452 100644
--- a/src/graph.rs
+++ b/src/graph.rs
@@ -500,41 +500,20 @@ fn label_propagation(
     labels
 }
 
-/// Schema fit: for a node, measure how well-connected its neighbors are
-/// to each other. High density + high CC among neighbors = good schema fit.
+/// Schema fit for a node: how well-integrated into its local neighborhood.
+///
+/// Algebraically identical to clustering_coefficient — both measure the
+/// fraction of neighbor pairs that are connected. Kept as an alias for
+/// semantic clarity: CC is a graph metric, schema fit is a cognitive one
+/// (how well does this node fit the surrounding schema?).
 pub fn schema_fit(graph: &Graph, key: &str) -> f32 {
-    let neighbors = graph.neighbor_keys(key);
-    let n = neighbors.len();
-    if n < 2 {
-        return 0.0; // isolated or leaf — no schema context
-    }
-
-    // Count edges among neighbors
-    let neighbor_vec: Vec<&str> = neighbors.iter().copied().collect();
-    let mut inter_edges = 0u32;
-    for i in 0..neighbor_vec.len() {
-        for j in (i + 1)..neighbor_vec.len() {
-            let ni_neighbors = graph.neighbor_keys(neighbor_vec[i]);
-            if ni_neighbors.contains(neighbor_vec[j]) {
-                inter_edges += 1;
-            }
-        }
-    }
-
-    let max_edges = (n * (n - 1)) / 2;
-    let density = if max_edges == 0 { 0.0 } else {
-        inter_edges as f32 / max_edges as f32
-    };
-
-    // Combine neighborhood density with own CC
-    let cc = graph.clustering_coefficient(key);
-    (density + cc) / 2.0
+    graph.clustering_coefficient(key)
 }
 
 /// Compute schema fit for all nodes
 pub fn schema_fit_all(graph: &Graph) -> HashMap<String, f32> {
     graph.nodes().iter()
-        .map(|key| (key.clone(), schema_fit(graph, key)))
+        .map(|key| (key.clone(), graph.clustering_coefficient(key)))
         .collect()
 }