fix: notes - last commit for GSoC2025

Author: BeaMarton13

doc/source/community_detection_guide/notebooks/community_detection_algorithms.ipynb

@@ -10,7 +10,7 @@
     "Modularity maximization methods are a prominent class of algorithms in community detection that aim to discover partitions of a network by optimizing a specific quality function called modularity.\n",
     "<div style=\"background-color: #e6ffe6; padding: 0px; border-radius: 5px;\">\n",
     "    \n",
-    "**NOTE:** You can find a more detailed explanation of **modularity** [here](./modularityZ.ipynb).\n",
+    "_Note:_ You can find a more detailed explanation of **modularity** [here](./modularityZ.ipynb).\n",
     "\n",
     "</div>\n",
     "\n",
@@ -68,7 +68,7 @@
     "These methods don't require you to pre-specify the number of communities. Instead, you choose the final partition by cutting the dendrogram at a particular level, which defines the number and size of the resulting communities.\n",
     "<div style=\"background-color: #e6ffe6; padding: 0px; border-radius: 5px;\">\n",
     "    \n",
-    "**NOTE:** You can find a more detailed explanation of **hierarchical clustering** [here](./hierarchical_clustering.ipynb).\n",
+    "_Note:_ You can find a more detailed explanation of **hierarchical clustering** [here](./hierarchical_clustering.ipynb).\n",
     "\n",
     "</div>\n",
     "\n",
@@ -145,7 +145,7 @@
     "\n",
     "- _When Simplicity and Interpretability of the Core Mechanism are Valued:_ The underlying principle of labels propagating and consolidating within dense regions is intuitive. For applications where a straightforward, graph-driven clustering method is preferred over complex optimization functions, LPA is a strong candidate.\n",
     "\n",
-    "- _When Non-Overlapping Communities are Acceptable (or Required):_ The basic LPA assigns each node to exactly one community. If your domain or problem requires distinct, mutually exclusive groups, LPA naturally provides this. (Note: Variants exist for overlapping communities, but the standard LPA does not produce them.)\n"
+    "- _When Non-Overlapping Communities are Acceptable (or Required):_ The basic LPA assigns each node to exactly one community. If your domain or problem requires distinct, mutually exclusive groups, LPA naturally provides this. (_Note:_ Variants exist for overlapping communities, but the standard LPA does not produce them.)\n"
    ]
   },
   {
@@ -285,7 +285,7 @@
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-       "<table id=\"itables_75c81ab6_ca9f_4959_8b2f_473b29b55ef3\"><tbody><tr>\n",
+       "<table id=\"itables_2086ef8c_9ee2_4b2c_9427_c3e259664091\"><tbody><tr>\n",
        "    <td style=\"vertical-align:middle; text-align:left\">\n",
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@@ -383,11 +383,11 @@
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        "\n",
-       "    document.querySelectorAll(\"#itables_75c81ab6_ca9f_4959_8b2f_473b29b55ef3:not(.dataTable)\").forEach(table => {\n",
+       "    document.querySelectorAll(\"#itables_2086ef8c_9ee2_4b2c_9427_c3e259664091:not(.dataTable)\").forEach(table => {\n",
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+       "        let dt_args = {\"scrollY\": \"300px\", \"scrollCollapse\": true, \"fixedColumns\": true, \"pageLength\": -1, \"layout\": {\"topStart\": \"pageLength\", \"topEnd\": \"search\", \"bottomStart\": \"info\", \"bottomEnd\": \"paging\"}, \"text_in_header_can_be_selected\": true, \"classes\": [\"display\", \"nowrap\"], \"style\": {\"table-layout\": \"auto\", \"width\": \"auto\", \"margin\": \"auto\", \"caption-side\": \"bottom\"}, \"order\": [], \"table_html\": \"<table><thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      \\n      <th>Method</th>\\n      <th>Function in igraph (Python)</th>\\n      <th>Directed Graph Support</th>\\n      <th>Weighted Graph Support</th>\\n      <th>Signed Graph Support</th>\\n      <th>Sparse Graph Performance</th>\\n      <th>Dense Graph Performance</th>\\n      <th>Deterministic</th>\\n    </tr>\\n  </thead></table>\", \"data_json\": \"[[\\\"Edge Betweenness\\\", \\\"`Graph.community_edge_betweenness()`\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c (Slow for large)\\\", \\\"\\\\u2705\\\"], [\\\"Fast-Greedy\\\", \\\"`Graph.community_fastgreedy()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705 (Very efficient)\\\", \\\"\\\\u2705 (Can handle)\\\", \\\"\\\\u274c\\\"], [\\\"Fluid Communities\\\", \\\"`Graph.community_fluid_communities()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u274c (weights ignored)\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\"], [\\\"Infomap\\\", \\\"`Graph.community_infomap()`\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\"], [\\\"Label Propagation\\\", \\\"`Graph.community_label_propagation()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\"], [\\\"Leading Eigenvector\\\", \\\"`Graph.community_leading_eigenvector()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\"], [\\\"Leiden\\\", \\\"`Graph.community_leiden()`\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\"], [\\\"Louvain (Multilevel)\\\", \\\"`Graph.community_multilevel()`\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\"], [\\\"Spinglass\\\", \\\"`Graph.community_spinglass()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c (Slower)\\\", \\\"\\\\u274c\\\"], [\\\"Walktrap\\\", \\\"`Graph.community_walktrap()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\"], [\\\"Optimal Modularity\\\", \\\"`Graph.community_optimal_modularity()`\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u274c (Small graphs only)\\\", \\\"\\\\u274c (Small graphs only)\\\", \\\"\\\\u2705\\\"], [\\\"Voronoi\\\", \\\"`Graph.community_voronoi()`\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u274c\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\", \\\"\\\\u2705\\\"]]\"};\n",
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doc/source/community_detection_guide/notebooks/functions.ipynb

@@ -27,7 +27,7 @@
    "id": "75c4b640-d999-4947-bdcd-4c2dc84b7466",
    "metadata": {},
    "source": [
-    "*Note:* igraph has two stable plotting backends at the moment: Cairo and Matplotlib. It also has experimental support for plotly. The Cairo backend is dependent on the pycairo or cairocffi libraries that provide Python bindings to the popular Cairo library. In our case we want to use the matplotlib backend to avoid additional dependencies, so it is useful to fix the plotting.backend in the config in each notebook we want to use igraph's plotting functions.\n",
+    "_Note:_ igraph has two stable plotting backends at the moment: Cairo and Matplotlib. It also has experimental support for plotly. The Cairo backend is dependent on the pycairo or cairocffi libraries that provide Python bindings to the popular Cairo library. In our case we want to use the matplotlib backend to avoid additional dependencies, so it is useful to fix the plotting.backend in the config in each notebook we want to use igraph's plotting functions.\n",
     "\n",
     "---\n",
     "Temporarily set the random state, restoring it afterwards.\n",

doc/source/community_detection_guide/notebooks/generating_and_visualizing_clusters.ipynb

@@ -67,7 +67,7 @@
    "metadata": {},
    "source": [
     "#### Visualizing the dendrogram\n",
-    "_NOTE:_ The orientation can be changed (e.g., \"top-bottom\", \"left-right\")"
+    "_Note:_ The orientation can be changed (e.g., \"top-bottom\", \"left-right\")"
    ]
   },
   {
@@ -105,7 +105,7 @@
     "The `as_clustering()` method returns a `VertexClustering` object, which represents the partition with the highest modularity found by the algorithm among all available partitions.\n",
     "<div style=\"background-color: #e6ffe6; border-radius: 5px;\">\n",
     "    \n",
-    "**NOTE:** You can find more details about how to work with clusterings [here](./working_with_clusterings.ipynb).\n",
+    "_Note:_ You can find more details about how to work with clusterings [here](./working_with_clusterings.ipynb).\n",
     "\n",
     "</div>"
    ]

doc/source/community_detection_guide/notebooks/hierarchical_clustering.ipynb

@@ -243,7 +243,7 @@
    "id": "f3981bc5-f6bf-4b41-a168-cae9c17ec764",
    "metadata": {},
    "source": [
-    "*Note:* Based on our previous analysis, the \"good\" partitioning yields a significantly higher modularity score. It is important to note, however, that a high modularity score is not always a definitive indicator of a better community partitioning, as was previously demonstrated with the Grid Graph [here](test_significance_of_community.ipynb).\n",
+    "_Note:_ Based on our previous analysis, the \"good\" partitioning yields a significantly higher modularity score. It is important to note, however, that a high modularity score is not always a definitive indicator of a better community partitioning, as was previously demonstrated with the Grid Graph [here](test_significance_of_community.ipynb).\n",
     "\n",
     "## Directed modularity\n",
     "\n",