refactor: deduplicate clustering coefficient + BFS into GraphUtils

Extract clusteringCoefficient() and avgClusteringCoefficient() into
GraphUtils, replacing 5 independent inline implementations across
CsvReportExporter, GraphStatsDashboard, GraphStorytellerExporter,
GrowthRateAnalyzer. Also replace inline BFS in GraphDrawingQualityAnalyzer
with GraphUtils.bfsDistances, and inline component counting in
GraphStatsDashboard with GraphUtils.findComponents.

Removes ~60 lines of duplicated graph traversal code.

Author: sauravbhattacharya001

Gvisual/src/gvisual/CsvReportExporter.java

@@ -257,22 +257,7 @@ private Map<String, Integer> computeCommunities() {
     private Map<String, Double> computeClusteringCoefficients() {
         Map<String, Double> result = new LinkedHashMap<String, Double>();
         for (String v : graph.getVertices()) {
-            Collection<String> neighbors = graph.getNeighbors(v);
-            List<String> nList = new ArrayList<String>(neighbors);
-            int k = nList.size();
-            if (k < 2) {
-                result.put(v, 0.0);
-                continue;
-            }
-            int triangles = 0;
-            for (int i = 0; i < k; i++) {
-                for (int j = i + 1; j < k; j++) {
-                    if (graph.isNeighbor(nList.get(i), nList.get(j))) {
-                        triangles++;
-                    }
-                }
-            }
-            result.put(v, (2.0 * triangles) / (k * (k - 1)));
+            result.put(v, GraphUtils.clusteringCoefficient(graph, v));
         }
         return result;
     }

Gvisual/src/gvisual/GraphDrawingQualityAnalyzer.java

@@ -498,28 +498,10 @@ private void ensureDistCache(List<String> verts) {
         if (distCache != null) return;
         distCache = new HashMap<>();
         for (String s : verts) {
-            distCache.put(s, bfs(s));
+            distCache.put(s, GraphUtils.bfsDistances(graph, s));
         }
     }
 
-    private Map<String, Integer> bfs(String source) {
-        Map<String, Integer> dist = new HashMap<>();
-        dist.put(source, 0);
-        Queue<String> queue = new ArrayDeque<>();
-        queue.add(source);
-        while (!queue.isEmpty()) {
-            String u = queue.poll();
-            int d = dist.get(u);
-            for (String nbr : graph.getNeighbors(u)) {
-                if (!dist.containsKey(nbr)) {
-                    dist.put(nbr, d + 1);
-                    queue.add(nbr);
-                }
-            }
-        }
-        return dist;
-    }
-
     private static double convexHullArea(List<Point2D> points) {
         if (points.size() < 3) return 0;
 

Gvisual/src/gvisual/GraphStatsDashboard.java

@@ -84,18 +84,7 @@ private List<Map.Entry<String, Integer>> topVerticesByDegree(int n) {
     }
 
     private double clusteringCoefficient(String v) {
-        Collection<String> neighbors = graph.getNeighbors(v);
-        if (neighbors == null) return 0;
-        List<String> nList = new ArrayList<>(neighbors);
-        int k = nList.size();
-        if (k < 2) return 0;
-        int links = 0;
-        for (int i = 0; i < k; i++) {
-            for (int j = i + 1; j < k; j++) {
-                if (graph.isNeighbor(nList.get(i), nList.get(j))) links++;
-            }
-        }
-        return (2.0 * links) / (k * (k - 1));
+        return GraphUtils.clusteringCoefficient(graph, v);
     }
 
     private Map<String, Integer> edgeTypeBreakdown() {
@@ -121,30 +110,11 @@ private double avgDegree() {
     }
 
     private double avgClusteringCoefficient() {
-        if (graph.getVertexCount() == 0) return 0;
-        double sum = 0;
-        for (String v : graph.getVertices()) sum += clusteringCoefficient(v);
-        return sum / graph.getVertexCount();
+        return GraphUtils.avgClusteringCoefficient(graph);
     }
 
     private int connectedComponents() {
-        Set<String> visited = new HashSet<>();
-        int count = 0;
-        for (String v : graph.getVertices()) {
-            if (!visited.contains(v)) {
-                count++;
-                Queue<String> queue = new ArrayDeque<>();
-                queue.add(v);
-                visited.add(v);
-                while (!queue.isEmpty()) {
-                    String cur = queue.poll();
-                    for (String nb : graph.getNeighbors(cur)) {
-                        if (visited.add(nb)) queue.add(nb);
-                    }
-                }
-            }
-        }
-        return count;
+        return GraphUtils.findComponents(graph).size();
     }
 
     // ── clustering coefficient histogram bins ────────────────────────

Gvisual/src/gvisual/GraphStorytellerExporter.java

@@ -272,23 +272,7 @@ private List<Set<String>> findComponents() {
     }
 
     private double computeAvgClustering() {
-        double total = 0;
-        int count = 0;
-        for (String v : graph.getVertices()) {
-            Collection<String> neighbors = graph.getNeighbors(v);
-            int k = neighbors.size();
-            if (k < 2) continue;
-            List<String> nbList = new ArrayList<>(neighbors);
-            int links = 0;
-            for (int i = 0; i < nbList.size(); i++) {
-                for (int j = i + 1; j < nbList.size(); j++) {
-                    if (graph.isNeighbor(nbList.get(i), nbList.get(j))) links++;
-                }
-            }
-            total += (2.0 * links) / (k * (k - 1));
-            count++;
-        }
-        return count > 0 ? total / count : 0;
+        return GraphUtils.avgClusteringCoefficient(graph);
     }
 
     private long countTriangles() {

Gvisual/src/gvisual/GraphUtils.java

@@ -333,6 +333,49 @@ public static int cycleRankOfSubgraph(
         return edges - vertices.size() + comps;
     }
 
+    // ── Clustering coefficient ────────────────────────────────────────
+
+    /**
+     * Computes the local clustering coefficient for a single vertex.
+     * <p>C(v) = 2 * triangles(v) / (deg(v) * (deg(v) - 1))</p>
+     *
+     * @param graph the JUNG graph
+     * @param v     the vertex
+     * @return clustering coefficient in [0, 1], or 0 if degree &lt; 2
+     */
+    public static double clusteringCoefficient(
+            Graph<String, Edge> graph, String v) {
+        Collection<String> neighbors = graph.getNeighbors(v);
+        if (neighbors == null) return 0.0;
+        List<String> nList = new ArrayList<String>(neighbors);
+        int k = nList.size();
+        if (k < 2) return 0.0;
+        int links = 0;
+        for (int i = 0; i < k; i++) {
+            for (int j = i + 1; j < k; j++) {
+                if (graph.isNeighbor(nList.get(i), nList.get(j))) links++;
+            }
+        }
+        return (2.0 * links) / (k * (k - 1));
+    }
+
+    /**
+     * Computes the average clustering coefficient across all vertices.
+     *
+     * @param graph the JUNG graph
+     * @return average clustering coefficient, or 0 if the graph is empty
+     */
+    public static double avgClusteringCoefficient(
+            Graph<String, Edge> graph) {
+        int n = graph.getVertexCount();
+        if (n == 0) return 0.0;
+        double sum = 0.0;
+        for (String v : graph.getVertices()) {
+            sum += clusteringCoefficient(graph, v);
+        }
+        return sum / n;
+    }
+
     // ── Graph copy ──────────────────────────────────────────────────────
 
     /**

Gvisual/src/gvisual/GrowthRateAnalyzer.java

@@ -116,29 +116,6 @@ private static double computeDensity(int nodes, int edges) {
     }
 
     private static double computeAvgClustering(Graph<String, Edge> g) {
-        if (g.getVertexCount() == 0) return 0.0;
-
-        Map<String, Set<String>> adj = GraphUtils.buildAdjacencyMap(g);
-        double totalClustering = 0.0;
-        for (String v : g.getVertices()) {
-            Set<String> neighbors = adj.get(v);
-            if (neighbors == null) continue;
-            List<String> neighborList = new ArrayList<>(neighbors);
-            int k = neighborList.size();
-            if (k < 2) continue;
-
-            int triangles = 0;
-            for (int i = 0; i < k; i++) {
-                Set<String> ni = adj.get(neighborList.get(i));
-                for (int j = i + 1; j < k; j++) {
-                    if (ni != null && ni.contains(neighborList.get(j))) {
-                        triangles++;
-                    }
-                }
-            }
-            double maxTriangles = (double) k * (k - 1) / 2.0;
-            totalClustering += triangles / maxTriangles;
-        }
-        return totalClustering / g.getVertexCount();
+        return GraphUtils.avgClusteringCoefficient(g);
     }
 }