diff --git a/doc/examples_sphinx-gallery/personalized_pagerank.py b/doc/examples_sphinx-gallery/personalized_pagerank.py
new file mode 100644
index 000000000..2fd044612
--- /dev/null
+++ b/doc/examples_sphinx-gallery/personalized_pagerank.py
@@ -0,0 +1,96 @@
+"""
+.. _tutorials-personalized_pagerank:
+
+===============================
+Personalized PageRank on a grid
+===============================
+
+This example demonstrates how to calculate and visualize personalized PageRank on a grid. We use the :meth:`igraph.Graph.personalized_pagerank` method, and demonstrate the effects on a grid graph.
+"""
+
+# %%
+# .. note::
+#
+#    The PageRank score of a vertex reflects the probability that a random walker will be at that vertex over the long run. At each step the walker has a 1 - damping chance to restart the walk and pick a starting vertex according to the probabilities defined in the reset vector.
+
+import igraph as ig
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+import numpy as np
+
+# %%
+# We define a function that plots the graph on a Matplotlib axis, along with
+# its personalized PageRank values. The function also generates a
+# color bar on the side to see how the values change.
+# We use `Matplotlib's Normalize class <https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.Normalize.html>`_
+# to set the colors and ensure that our color bar range is correct.
+
+
+def plot_pagerank(graph: ig.Graph, p_pagerank: list[float]):
+    """Plots personalized PageRank values on a grid graph with a colorbar.
+
+    Parameters
+    ----------
+    graph : ig.Graph
+        graph to plot
+    p_pagerank : list[float]
+        calculated personalized PageRank values
+    """
+    # Create the axis for matplotlib
+    _, ax = plt.subplots(figsize=(8, 8))
+
+    # Create a matplotlib colormap
+    # coolwarm goes from blue (lowest value) to red (highest value)
+    cmap = cm.coolwarm
+
+    # Normalize the PageRank values for colormap
+    normalized_pagerank = ig.rescale(p_pagerank)
+
+    graph.vs["color"] = [cmap(pr) for pr in normalized_pagerank]
+    graph.vs["size"] = ig.rescale(p_pagerank, (20, 40))
+    graph.es["color"] = "gray"
+    graph.es["width"] = 1.5
+
+    # Plot the graph
+    ig.plot(graph, target=ax, layout=graph.layout_grid())
+
+    # Add a colorbar
+    sm = cm.ScalarMappable(norm=plt.Normalize(min(p_pagerank), max(p_pagerank)), cmap=cmap)
+    plt.colorbar(sm, ax=ax, label="Personalized PageRank")
+
+    plt.title("Graph with Personalized PageRank")
+    plt.axis("equal")
+    plt.show()
+
+
+# %%
+# First, we generate a graph, e.g. a Lattice Graph, which basically is a ``dim x dim`` grid:
+dim = 5
+grid_size = (dim, dim)  # dim rows, dim columns
+g = ig.Graph.Lattice(dim=grid_size, circular=False)
+
+# %%
+# Then we initialize the ``reset_vector`` (it's length should be equal to the number of vertices in the graph):
+reset_vector = np.zeros(g.vcount())
+
+# %%
+# Then we set the nodes to prioritize, for example nodes with indices ``0`` and ``18``:
+reset_vector[0] = 1
+reset_vector[18] = 0.65
+
+# %%
+# Then we calculate the personalized PageRank:
+personalized_page_rank = g.personalized_pagerank(damping=0.85, reset=reset_vector)
+
+# %%
+# Finally, we plot the graph with the personalized PageRank values:
+plot_pagerank(g, personalized_page_rank)
+
+
+# %%
+# Alternatively, we can play around with the ``damping`` parameter:
+personalized_page_rank = g.personalized_pagerank(damping=0.45, reset=reset_vector)
+
+# %%
+# Here we can see the same plot with the new damping parameter:
+plot_pagerank(g, personalized_page_rank)