diff --git a/notebooks/cugraph_benchmarks/synth_release.ipynb b/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb
similarity index 77%
rename from notebooks/cugraph_benchmarks/synth_release.ipynb
rename to notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb
index 18979f3ecee..1acef5d558b 100644
--- a/notebooks/cugraph_benchmarks/synth_release.ipynb
+++ b/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb
@@ -40,7 +40,8 @@
     "| Author        |    Date    |  Update             | cuGraph Version |  Test Hardware         |\n",
     "| --------------|------------|---------------------|-----------------|------------------------|\n",
     "| Don Acosta    | 1/12/2023  | Created             | 23.02 nightly   | RTX A6000, CUDA 11.7   |\n",
-    "| Brad Rees     | 1/27/2023  | Modified            | 23.02 nightly   | RTX A6000, CUDA 11.7   |\n"
+    "| Brad Rees     | 1/27/2023  | Modified            | 23.02 nightly   | RTX A6000, CUDA 11.7   |\n",
+    "| Naim, Md      | 2/12/2024  | Modified            | 24.04 nightly   | RTX A6000, CUDA 12.0   |\n"
    ]
   },
   {
@@ -124,12 +125,11 @@
     "import gc\n",
     "import os\n",
     "from time import perf_counter\n",
-    "import numpy as np\n",
-    "import math\n",
+    "import pandas as pd\n",
+    "from collections import defaultdict\n",
     "\n",
     "# rapids\n",
     "import cugraph\n",
-    "import cudf\n",
     "\n",
     "# NetworkX libraries\n",
     "import networkx as nx\n",
@@ -212,7 +212,7 @@
     "\n",
     "\n",
     "# Which dataset is to be used\n",
-    "data = data_full\n"
+    "data = data_quick\n"
    ]
   },
   {
@@ -518,16 +518,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def nx_bfs(_G):\n",
-    "    seed = 0\n",
+    "def nx_bfs(_G, seed):\n",
     "    t1 = perf_counter()\n",
-    "    nb = nx.bfs_edges(_G, seed)\n",
-    "    nb_list = list(nb) # gen -> list\n",
+    "    _ = nx.bfs_edges(_G, seed)\n",
     "    t2 = perf_counter() - t1\n",
     "    return t2\n",
     "\n",
-    "def cu_bfs(_G):\n",
-    "    seed = 0\n",
+    "def cu_bfs(_G, seed):\n",
     "    t1 = perf_counter()\n",
     "    _ = cugraph.bfs(_G, seed)\n",
     "    t2 = perf_counter() - t1\n",
@@ -547,17 +544,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def nx_sssp(_G):\n",
-    "    seed = 0\n",
+    "def nx_sssp(_G, seed):\n",
     "    t1 = perf_counter()\n",
-    "    _ = nx.shortest_path(_G, seed)\n",
+    "    if nx.is_weighted(_G):\n",
+    "        _ = nx.shortest_path(_G, seed)\n",
+    "    else:\n",
+    "        _ = nx.bfs_edges(_G, seed)\n",
     "    t2 = perf_counter() - t1\n",
     "    return t2\n",
     "\n",
-    "def cu_sssp(_G):\n",
-    "    seed = 0\n",
+    "def cu_sssp(_G, seed):\n",
     "    t1 = perf_counter()\n",
-    "    _ = cugraph.sssp(_G, seed)\n",
+    "    if _G.weighted:\n",
+    "        _ = cugraph.sssp(_G, seed)\n",
+    "    else:\n",
+    "        _ = cugraph.bfs(_G, seed)\n",
     "    t2 = perf_counter() - t1\n",
     "    return t2\n"
    ]
@@ -571,6 +572,27 @@
     "# Benchmark"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Placeholders to collect algorithm run statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cugraph_algo_run_times = defaultdict(defaultdict)\n",
+    "nx_algo_run_times = defaultdict(defaultdict)\n",
+    "cugraph_graph_creation_times = defaultdict()\n",
+    "nx_graph_creation_times = defaultdict()\n",
+    "perf_algos = defaultdict(defaultdict)\n",
+    "perf = defaultdict(defaultdict)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -587,79 +609,50 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# arrays to capture performance gains\n",
-    "names = []\n",
-    "algos = []\n",
-    "graph_create_cu = []\n",
-    "graph_create_nx = []\n",
-    "\n",
-    "# Two dimension data [file, perf]\n",
-    "time_algo_nx = []          # NetworkX\n",
-    "time_algo_cu = []          # cuGraph\n",
-    "perf = []\n",
-    "perf_algo = []\n",
     "\n",
-    "algos.append(\"   \")\n",
-    "\n",
-    "i = 0\n",
-    "for k,v in data.items():\n",
-    "    # init all the 2-d arrays\n",
-    "    time_algo_nx.append([])\n",
-    "    time_algo_cu.append([])\n",
-    "    perf.append([])\n",
-    "    perf_algo.append([])\n",
-    "\n",
-    "    # Saved the file Name\n",
-    "    names.append(k)\n",
+    "for dataset, scale in data.items():\n",
     "\n",
     "    # generate data\n",
     "    print(\"------------------------------\")\n",
-    "    print(f'Creating Graph of Scale = {v}')\n",
-    "\n",
-    "    gdf = generate_data(v)\n",
+    "    print(f'Creating Graph of Scale = {scale}')\n",
+    "    \n",
+    "    gdf = generate_data(scale)\n",
     "    pdf = gdf.to_pandas()\n",
-    "    print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n",
     "\n",
-    "    # create the graphs\n",
+    "    print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n",
+    "    \n",
+    "    # create cuGraph and NX graphs\n",
     "    g_cu, tcu = create_cu_graph(gdf)\n",
     "    g_nx, tnx = create_nx_graph(pdf)\n",
-    "    graph_create_cu.append(tcu)\n",
-    "    graph_create_nx.append(tnx)\n",
+    "    cugraph_graph_creation_times[dataset] = tcu\n",
+    "    nx_graph_creation_times[dataset] = tnx\n",
     "    del gdf, pdf\n",
     "\n",
     "    # prep\n",
     "    deg = g_cu.degree()\n",
     "    deg_max = deg['degree'].max()\n",
-    "\n",
     "    alpha = 1 / deg_max\n",
     "    num_nodes = g_cu.number_of_vertices()\n",
-    "\n",
     "    del deg\n",
     "    gc.collect()\n",
     "\n",
-    "    #----- Algorithm order is same as defined at top ----\n",
-    "\n",
     "    #-- Katz \n",
-    "    print(\"\\tKatz  \", end = '')\n",
-    "    if i == 0: \n",
-    "        algos.append(\"Katz\")\n",
-    "\n",
+    "    algorithm = \"Katz\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
     "    tx = nx_katz(g_nx, alpha)\n",
     "    print(\"c.\", end='')\n",
     "    tc = cu_katz(g_cu, alpha)\n",
     "    print(\"\")\n",
-    "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    \n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- BC\n",
-    "    print(\"\\tBC k=100  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"BC Estimate fixed\")\n",
-    "\n",
+    "    algorithm = \"BC\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    k = 100\n",
     "    if k > num_nodes:\n",
     "        k = int(num_nodes)\n",
@@ -668,80 +661,70 @@
     "    print(\"c.\", end='')\n",
     "    tc = cu_bc(g_cu, k)\n",
     "    print(\" \")\n",
-    "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- Louvain\n",
-    "    print(\"\\tLouvain  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"Louvain\")\n",
-    "\n",
+    "    algorithm = \"Louvain\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
     "    tx = nx_louvain(g_nx)\n",
     "    print(\"c.\", end='')\n",
     "    tc = cu_louvain(g_cu)\n",
     "    print(\" \")\n",
     "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- TC\n",
-    "    print(\"\\tTC  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"TC\")\n",
-    "\n",
+    "    algorithm = \"TC\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
     "    tx = nx_tc(g_nx)\n",
     "    print(\"c.\", end='')\n",
     "    tc = cu_tc(g_cu)\n",
     "    print(\" \")\n",
-    "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    \n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- Core Number\n",
-    "    print(\"\\tCore Number  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"Core Number\")\n",
-    "\n",
+    "    algorithm = \"Core Number\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
     "    tx = nx_core_num(g_nx)\n",
     "    print(\"c.\", end='')\n",
     "    tc = cu_core_num(g_cu)\n",
     "    print(\" \")\n",
     "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- PageRank\n",
-    "    print(\"\\tPageRank  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"PageRank\")\n",
-    "\n",
+    "    algorithm = \"PageRank\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
     "    tx = nx_pagerank(g_nx)\n",
     "    print(\"c.\", end='')\n",
     "    tc = cu_pagerank(g_cu)\n",
     "    print(\" \")\n",
     "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- Jaccard\n",
-    "    print(\"\\tJaccard  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"Jaccard\")\n",
+    "    algorithm = \"Jaccard\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "\n",
     "    print(\"n.\", end='')\n",
     "    tx = nx_jaccard(g_nx)\n",
@@ -749,46 +732,44 @@
     "    tc = cu_jaccard(g_cu)\n",
     "    print(\" \")\n",
     "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
-    "    #-- BFS\n",
-    "    print(\"\\tBFS  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"BFS\")\n",
+    "    # Seed for BFS and SSSP\n",
+    "    nx_seed = list(g_nx.nodes)[0]\n",
+    "    cu_seed = g_cu.nodes().to_pandas().iloc[0]\n",
     "\n",
+    "    #-- BFS\n",
+    "    algorithm = \"BFS\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
-    "    tx = nx_bfs(g_nx)\n",
+    "    tx = nx_bfs(g_nx, seed=nx_seed)\n",
     "    print(\"c.\", end='')\n",
-    "    tc = cu_bfs(g_cu)\n",
+    "    tc = cu_bfs(g_cu, seed=cu_seed)\n",
     "    print(\" \")\n",
     "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
     "    #-- SSSP\n",
-    "    print(\"\\tSSSP  \", end='')\n",
-    "    if i == 0:\n",
-    "        algos.append(\"SSP\")\n",
-    "\n",
+    "    algorithm = \"SSSP\"\n",
+    "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
-    "    tx = nx_sssp(g_nx)\n",
+    "    tx = nx_sssp(g_nx, seed=nx_seed)\n",
+    "\n",
     "    print(\"c.\", end='')\n",
-    "    tc = cu_sssp(g_cu)\n",
+    "    tc = cu_sssp(g_cu, seed=cu_seed)\n",
     "    print(\" \")\n",
     "\n",
-    "    time_algo_nx[i].append(tx)\n",
-    "    time_algo_cu[i].append(tc)\n",
-    "    perf_algo[i].append ( (tx/tc) )\n",
-    "    perf[i].append( (tx + tnx) /  (tc + tcu) )\n",
+    "    nx_algo_run_times[dataset][algorithm] = tx\n",
+    "    cugraph_algo_run_times[dataset][algorithm] = tc\n",
+    "    perf_algos[dataset][algorithm] = tx/tc \n",
+    "    perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n",
     "\n",
-    "    # increament count\n",
-    "    i = i + 1\n",
-    "    \n",
     "    del g_cu, g_nx\n",
     "    gc.collect()\n"
    ]
@@ -799,13 +780,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Print results\n",
-    "print(algos)\n",
-    "\n",
-    "for i in range(num_datasets):\n",
-    "    print(f\"{names[i]}\")\n",
-    "    print(f\"{perf[i]}\")\n",
-    "    print(f\"{perf_algo[i]}\")"
+    "# Speedup\n",
+    "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX)------\\n\")\n",
+    "print(pd.DataFrame(perf))\n",
+    "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX, excluding graph creation time)------\\n\")\n",
+    "print(pd.DataFrame(perf_algos))"
    ]
   },
   {
@@ -814,15 +793,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Print results\n",
-    "print(\"\\n------------------------------\")\n",
-    "print(\"\\tAlgorithm Run times  (NX then cuGraph)\\n\")\n",
+    "# Nx and cuGraph execution times for different algorithms\n",
+    "nx_and_cugraph_run_times = pd.DataFrame()\n",
+    "for dataset in cugraph_algo_run_times.keys():\n",
+    "    temp_df = pd.DataFrame({'NX': nx_algo_run_times[dataset], 'cuGraph': cugraph_algo_run_times[dataset]})\n",
+    "    columns = [(dataset, 'cuGraph'), (dataset, 'NX')]\n",
+    "    temp_df.columns = pd.MultiIndex.from_tuples(columns)\n",
+    "    nx_and_cugraph_run_times = pd.concat([temp_df, nx_and_cugraph_run_times], axis=1)\n",
     "\n",
-    "print(algos)\n",
-    "for i in range(num_datasets):\n",
-    "    print(f\"{names[i]}\")\n",
-    "    print(f\"{time_algo_nx[i]}\")\n",
-    "    print(f\"{time_algo_cu[i]}\")"
+    "print(\"\\n\\t------cuGraph and NX execution times for different algorithms-----\\n\")\n",
+    "print(nx_and_cugraph_run_times)"
    ]
   },
   {
@@ -855,7 +835,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 15:55:03) \n[GCC 10.4.0]"
+   "version": "3.1.0"
   },
   "vscode": {
    "interpreter": {
diff --git a/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb b/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb
index c44f475c441..dd09830160f 100644
--- a/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb
+++ b/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb
@@ -39,9 +39,9 @@
     "    \n",
     "| Author        |    Date    |  Update             | cuGraph Version |  Test Hardware         |\n",
     "| --------------|------------|---------------------|-----------------|------------------------|\n",
-    "| Don Acosta    | 1/12/2023  | Created             | 23.02 nightly   | RTX A6000, CUDA 11.7   |\n",
-    "| Brad Rees     | 1/27/2023  | Modified            | 23.02 nightly   | RTX A6000, CUDA 11.7   |\n",
-    "| Naim, Md      | 2/08/2024  | Modified for SNMG   | 24.04 nightly   | RTX A6000, CUDA 12.0   |\n"
+    "| Don Acosta    | 1/12/2023  | Created             | 23.02 nightly   | 2xRTX A6000, CUDA 11.7 |\n",
+    "| Brad Rees     | 1/27/2023  | Modified            | 23.02 nightly   | 2xRTX A6000, CUDA 11.7 |\n",
+    "| Naim, Md      | 2/08/2024  | Modified for SNMG   | 24.04 nightly   | 2xRTX A6000, CUDA 12.0 |\n"
    ]
   },
   {
@@ -590,7 +590,10 @@
    "source": [
     "def nx_sssp(_G, seed):\n",
     "    t1 = perf_counter()\n",
-    "    _ = nx.shortest_path(_G, seed)\n",
+    "    if nx.is_weighted(_G):\n",
+    "        _ = nx.shortest_path(_G, seed)\n",
+    "    else:\n",
+    "        _ = nx.bfs_edges(_G, seed)\n",
     "    t2 = perf_counter() - t1\n",
     "    return t2\n",
     "\n",
@@ -599,9 +602,17 @@
     "    t1 = perf_counter()\n",
     "    # SSSP requires weighted graph\n",
     "    if mg:\n",
-    "        _ = cugraph.dask.bfs(_G, seed)\n",
+    "        if _G.weighted: \n",
+    "            _ = cugraph.dask.sssp(_G, seed)\n",
+    "        else:\n",
+    "            _ = cugraph.dask.bfs(_G, seed)\n",
+    "\n",
     "    else:\n",
-    "        _ = cugraph.bfs(_G, seed)\n",
+    "        if _G.weighted:\n",
+    "            _ = cugraph.ssp(_G, seed)\n",
+    "        else:\n",
+    "            _ = cugraph.bfs(_G, seed)\n",
+    "\n",
     "    t2 = perf_counter() - t1\n",
     "    return t2\n"
    ]
@@ -646,14 +657,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "nx_algo_run_times = defaultdict(defaultdict)\n",
     "cugraph_algo_run_times = defaultdict(defaultdict)\n",
-    "perf_algos = defaultdict(defaultdict)\n",
-    "perf = defaultdict(defaultdict)\n",
+    "nx_algo_run_times = defaultdict(defaultdict)\n",
     "cugraph_graph_creation_times = defaultdict()\n",
-    "nx_graph_creation_times = defaultdict()\n"
+    "nx_graph_creation_times = defaultdict()\n",
+    "perf_algos = defaultdict(defaultdict)\n",
+    "perf = defaultdict(defaultdict)"
    ]
   },
   {
@@ -811,9 +820,9 @@
     "    algorithm = \"BFS\"\n",
     "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
-    "    tx = nx_bfs(g_nx, nx_seed)\n",
+    "    tx = nx_bfs(g_nx, seed=nx_seed)\n",
     "    print(\"c.\", end='')\n",
-    "    tc = cu_bfs(g_cu, seed = cu_seed, mg=True)\n",
+    "    tc = cu_bfs(g_cu, seed=cu_seed, mg=True)\n",
     "    print(\" \")\n",
     "\n",
     "    nx_algo_run_times[dataset][algorithm] = tx\n",
@@ -825,10 +834,10 @@
     "    algorithm = \"SSSP\"\n",
     "    print(f\"\\t{algorithm}  \", end = '')\n",
     "    print(\"n.\", end='')\n",
-    "    tx = nx_sssp(g_nx, nx_seed)\n",
+    "    tx = nx_sssp(g_nx, seed=nx_seed)\n",
     "\n",
     "    print(\"c.\", end='')\n",
-    "    tc = cu_sssp(g_cu, seed = cu_seed, mg=True)\n",
+    "    tc = cu_sssp(g_cu, seed=cu_seed, mg=True)\n",
     "    print(\" \")\n",
     "\n",
     "    nx_algo_run_times[dataset][algorithm] = tx\n",
@@ -856,7 +865,7 @@
     "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX)------\\n\")\n",
     "print(pd.DataFrame(perf))\n",
     "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX, excluding graph creation time)------\\n\")\n",
-    "print(pd.DataFrame(perf_algos))\n"
+    "print(pd.DataFrame(perf_algos))"
    ]
   },
   {