diff --git a/demo/data/knn.csv b/demo/data/knn.csv
new file mode 100644
index 0000000..e48ca5b
--- /dev/null
+++ b/demo/data/knn.csv
@@ -0,0 +1,4740 @@
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diff --git a/demo/data/knn.png b/demo/data/knn.png
new file mode 100644
index 0000000..a5f6151
Binary files /dev/null and b/demo/data/knn.png differ
diff --git a/demo/data/plot.py b/demo/data/plot.py
new file mode 100644
index 0000000..827c6f0
--- /dev/null
+++ b/demo/data/plot.py
@@ -0,0 +1,34 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Read the data
+data = np.loadtxt("sparse.csv", delimiter=",", skiprows=1, dtype=float)
+
+# Read the edges
+edges = np.loadtxt("knn.csv", delimiter=",", dtype=int)
+
+# Plot the edges
+for edge in edges:
+    point1 = data[edge[0]]
+    point2 = data[edge[1]]
+    plt.plot([point1[0], point2[0]], [point1[1], point2[1]], c='salmon', linewidth=0.5)
+
+# Plot the data
+plt.scatter(data[:, 0], data[:, 1], c='darkred', marker='o', s=1, zorder=2)
+
+# Remove axis labels
+plt.xticks([])
+plt.yticks([])
+
+# Remove white space
+plt.gca().set_axis_off()
+plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
+            hspace = 0, wspace = 0)
+plt.margins(0,0)
+plt.gca().xaxis.set_major_locator(plt.NullLocator())
+plt.gca().yaxis.set_major_locator(plt.NullLocator())
+plt.gca().set_aspect('equal', adjustable='box')
+
+# Save the plot
+plt.tight_layout()
+plt.savefig("knn.png", dpi=300, transparent=True)
\ No newline at end of file
diff --git a/demo/data/plot_tree.py b/demo/data/plot_tree.py
deleted file mode 100644
index c878717..0000000
--- a/demo/data/plot_tree.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import matplotlib.pyplot as plt
-
-
-# Read the data
-lines = open("tree.csv").readlines()
-
-points = {}
-bubbles = {}
-for row in lines:
-    s = row.split(",")
-    id = int(s[0])
-    height = int(s[1])
-    if height == 0:
-        x, y = float(s[-2]), float(s[-1])
-        points[id] = (x, y)
-    elif height == 1:
-        radius = float(s[-3])
-        x, y = float(s[-2]), float(s[-1])
-        children = s[3].split("+")
-        bubbles[id] = (x, y, radius, children)
-
-fig, ax = plt.subplots()
-colors = ['r', 'g', 'b', 'y', 'c', 'm']
-for id, (x, y, radius, children) in bubbles.items():
-    color = colors[id % len(colors)]
-    circle = plt.Circle((x, y), radius, color='b', alpha=0.25)
-    ax.add_artist(circle)
-    for child in children:
-        child = int(child)
-        x, y = points[child][0], points[child][1]
-        plt.scatter(x, y, color='b', s=2, marker='+')
-
-# remove coordinate axis names
-plt.xticks([])
-plt.yticks([])
-plt.gca().set_aspect('equal')
-
-title = "N = " + str(len(points)) + ", L = " + str(len(bubbles))
-plt.title(title)
-
-# set axis limits
-plt.savefig("tree.png", dpi=300)
\ No newline at end of file
diff --git a/demo/data/tree.csv b/demo/data/tree.csv
deleted file mode 100644
index e2a6a33..0000000
--- a/demo/data/tree.csv
+++ /dev/null
@@ -1,419 +0,0 @@
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diff --git a/demo/data/tree.png b/demo/data/tree.png
deleted file mode 100644
index 7a45ecd..0000000
Binary files a/demo/data/tree.png and /dev/null differ
diff --git a/demo/src/main.rs b/demo/src/main.rs
index dc8ec4c..9de13fd 100644
--- a/demo/src/main.rs
+++ b/demo/src/main.rs
@@ -1,6 +1,8 @@
-use rand::{rngs::StdRng, Rng, SeedableRng};
 use rindex::Rindex;
-use std::io::{BufRead, Write};
+use std::{
+    collections::HashMap,
+    io::{BufRead, Write},
+};
 
 fn main() {
     // Read the sparse dataset (https://www.kaggle.com/datasets/joonasyoon/clustering-exercises)
@@ -19,38 +21,26 @@ fn main() {
         data.push([x, y]);
     }
 
-    // Configure the tree
-    let mut tree = Rindex::new(5);
-    let mut point_ids = Vec::new();
-    let deletion_probability = 0.25; // 25%
+    // Configure the tree: we maintain 3 nearest neighbors for each point
+    let k = 10;
+    let mut tree = Rindex::new(k);
 
     // Perform random insertions and deletions
-    let mut rng = StdRng::seed_from_u64(0);
-    let mut num_insertions = 0;
-    let mut num_deletions = 0;
-    while !data.is_empty() {
-        let random = rng.gen::<f64>();
-        if random <= deletion_probability && !point_ids.is_empty() {
-            let random_id = rng.gen_range(0..point_ids.len());
-            let point_id = point_ids.swap_remove(random_id);
-            tree.delete(point_id);
-            num_deletions += 1;
-        } else {
-            let point = data.pop().unwrap();
-            let point_id = tree.insert(point);
-            point_ids.push(point_id);
-            num_insertions += 1;
-        }
+    let mut point_ids = HashMap::new();
+    for (order, point) in data.iter().enumerate() {
+        let point_id = tree.insert(*point);
+        point_ids.insert(point_id, order);
     }
-
-    println!("Insertions: {num_insertions}");
-    println!("Deletions: {num_deletions}");
     println!("Tree height: {}", tree.height());
 
-    let csv_rows = tree.nodes_to_string_rows();
-    let filename = "demo/data/tree.csv";
+    let filename = "demo/data/knn.csv";
     let mut file = std::fs::File::create(filename).unwrap();
-    for row in csv_rows {
-        file.write_all(row.as_bytes()).unwrap();
+    for (point_id, point_order) in &point_ids {
+        let (neighbors, _) = tree.neighbors_of(*point_id);
+        for neighbor in neighbors {
+            let neighbor_order = point_ids[&neighbor];
+            file.write_all(format!("{point_order},{neighbor_order}\n").as_bytes())
+                .unwrap();
+        }
     }
 }