diff --git a/notebooks/demo/nxcg_wikipedia_e2e.ipynb b/notebooks/demo/nxcg_wikipedia_e2e.ipynb
deleted file mode 100644
index 872c8860b91..00000000000
--- a/notebooks/demo/nxcg_wikipedia_e2e.ipynb
+++ /dev/null
@@ -1,219 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# `nx-cugraph` Demo - Wikipedia Pagerank\n",
-    "\n",
-    "This notebook demonstrates a zero code change, end-to-end workflow using `cudf.pandas` and `nx-cugraph`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Uncomment these two lines to enable GPU acceleration\n",
-    "# The rest of the code stays the same!\n",
-    "\n",
-    "# %load_ext cudf.pandas\n",
-    "# !NETWORKX_BACKEND_PRIORITY=cugraph\n",
-    "\n",
-    "import pandas as pd\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Downloading the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wget \"https://data.rapids.ai/cugraph/datasets/\"  # Use this command to download datasets from the web"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edgelist_csv = \"enwiki-20240620-edges.csv\"\n",
-    "nodedata_csv = \"enwiki-20240620-nodeids.csv\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Timed end-to-end code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read in the Wikipedia Connectivity data from `edgelist_csv`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time \n",
-    "edgelist_df = pd.read_csv(\n",
-    "    edgelist_csv,\n",
-    "    sep=\" \",\n",
-    "    names=[\"src\", \"dst\"],\n",
-    "    dtype=\"int32\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read in the Wikipedia pages metadata from `nodedata_csv`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "nodedata_df = pd.read_csv(\n",
-    "    nodedata_csv,\n",
-    "    sep=\"\\t\",\n",
-    "    names=[\"nodeid\", \"title\"],\n",
-    "    dtype={\"nodeid\": \"int32\", \"title\": \"str\"},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a NetworkX graph from the connectivity info we just loaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "G = nx.from_pandas_edgelist(\n",
-    "    edgelist_df,\n",
-    "    source=\"src\",\n",
-    "    target=\"dst\",\n",
-    "    create_using=nx.DiGraph,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the Pagerank algorithm on the NetworkX graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "nx_pr_vals = nx.pagerank(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a DataFrame containing the resulting pagerank values for each nodeid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "pagerank_df = pd.DataFrame({\n",
-    "    \"nodeid\": nx_pr_vals.keys(),\n",
-    "    \"pagerank\": nx_pr_vals.values()\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, add the NetworkX results to `nodedata` as a new column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "nodedata_df = nodedata_df.merge(pagerank_df, how=\"left\", on=\"nodeid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Showing the top 25 pages based on pagerank value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodedata_df.sort_values(by=\"pagerank\", ascending=False).head(25)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "devenv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}