Skip to content

254 potential problems with graphweighted outdegrees, fixes #254 #290

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
IngoScholtes merged 3 commits into from
Oct 2, 2025
Merged

254 potential problems with graphweighted outdegrees, fixes #254 #290

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0efcd51
new semantics of degree calculation
IngoScholtes Oct 2, 2025
32798bb
updted tutorial, fixing str type
IngoScholtes Oct 2, 2025
ba8b883
updated degree statistics
IngoScholtes Oct 2, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
209 changes: 174 additions & 35 deletions docs/tutorial/basic_concepts.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -171,9 +171,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EdgeIndex([[0, 0, 1],\n",
" [2, 1, 2]], sparse_size=(4, 4), nnz=3, sort_order=row)\n"
]
}
],
"source": [
"print(g.data.edge_index)"
]
Expand All @@ -194,7 +203,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 5,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -228,7 +237,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 6,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -257,7 +266,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 10,
"metadata": {},
"outputs": [
{
Expand All @@ -274,7 +283,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 11,
"metadata": {},
"outputs": [
{
Expand All @@ -298,7 +307,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -314,20 +323,20 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a\n",
"b\n",
"c\n",
"d\n",
"('a', 'c')\n",
"('a', 'b')\n",
"('b', 'c')\n"
"0\n",
"1\n",
"2\n",
"3\n",
"(0, 2)\n",
"(0, 1)\n",
"(1, 2)\n"
]
}
],
Expand All @@ -348,7 +357,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -364,7 +373,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 9,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -398,7 +407,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 11,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -435,7 +444,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 12,
"metadata": {},
"outputs": [
{
Expand All @@ -444,7 +453,7 @@
"tensor([2, 1])"
]
},
"execution_count": 22,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -609,7 +618,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 13,
"metadata": {},
"outputs": [
{
Expand All @@ -627,6 +636,136 @@
" print(f\"{v} -> {g.in_degrees[v]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `in_degree` and `out_degree` properties are shortcuts to a general `degree` function that can be used to calculate (weighted) in- and outdegrees. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'a': 0, 'c': 2, 'b': 1}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.degrees(mode='in')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'a': 2, 'c': 0, 'b': 1}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.degrees(mode='out')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Degrees can be alternatively returned as torch.tensors."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0, 2, 1], dtype=torch.int32)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.degrees(mode='in', return_tensor=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also use arbitrary numerical edge attributes that will be used for a weighted (in- or out) degree calculation."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"g.data.edge_weight=torch.tensor([1.0, 2.0, 3.0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0., 5., 1.])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.degrees(mode='in', edge_attr='edge_weight', return_tensor=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([3., 0., 3.])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.degrees(mode='out', edge_attr='edge_weight', return_tensor=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -636,16 +775,16 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Data(edge_index=[2, 3], num_nodes=3, node_sequence=[3, 1])"
"Data(edge_index=[2, 3], num_nodes=3, node_sequence=[3, 1], edge_weight=[3])"
]
},
"execution_count": 35,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -690,7 +829,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 16,
"metadata": {},
"outputs": [
{
Expand All @@ -700,8 +839,8 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_edge_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m g\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mis_cuda\n",
"File \u001b[0;32m/workspaces/pathpyG/src/pathpyG/core/graph.py:180\u001b[0m, in \u001b[0;36mGraph.from_edge_list\u001b[0;34m(edge_list, is_undirected, mapping, num_nodes, device)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_nodes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 177\u001b[0m num_nodes \u001b[38;5;241m=\u001b[39m mapping\u001b[38;5;241m.\u001b[39mnum_ids()\n\u001b[1;32m 179\u001b[0m edge_index \u001b[38;5;241m=\u001b[39m EdgeIndex(\n\u001b[0;32m--> 180\u001b[0m \u001b[43mmapping\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_idxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mcontiguous(),\n\u001b[1;32m 181\u001b[0m sparse_size\u001b[38;5;241m=\u001b[39m(num_nodes, num_nodes),\n\u001b[1;32m 182\u001b[0m is_undirected\u001b[38;5;241m=\u001b[39mis_undirected,\n\u001b[1;32m 183\u001b[0m )\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Graph(Data(edge_index\u001b[38;5;241m=\u001b[39medge_index, num_nodes\u001b[38;5;241m=\u001b[39mnum_nodes), mapping\u001b[38;5;241m=\u001b[39mmapping)\n",
"Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_edge_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m g\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mis_cuda\n",
"File \u001b[0;32m/workspaces/pathpyG/src/pathpyG/core/graph.py:179\u001b[0m, in \u001b[0;36mGraph.from_edge_list\u001b[0;34m(edge_list, is_undirected, mapping, device)\u001b[0m\n\u001b[1;32m 174\u001b[0m mapping \u001b[38;5;241m=\u001b[39m IndexMap(node_ids)\n\u001b[1;32m 176\u001b[0m num_nodes \u001b[38;5;241m=\u001b[39m mapping\u001b[38;5;241m.\u001b[39mnum_ids()\n\u001b[1;32m 178\u001b[0m edge_index \u001b[38;5;241m=\u001b[39m EdgeIndex(\n\u001b[0;32m--> 179\u001b[0m \u001b[43mmapping\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_idxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mcontiguous(),\n\u001b[1;32m 180\u001b[0m sparse_size\u001b[38;5;241m=\u001b[39m(num_nodes, num_nodes),\n\u001b[1;32m 181\u001b[0m is_undirected\u001b[38;5;241m=\u001b[39mis_undirected,\n\u001b[1;32m 182\u001b[0m )\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Graph(Data(edge_index\u001b[38;5;241m=\u001b[39medge_index, num_nodes\u001b[38;5;241m=\u001b[39mnum_nodes), mapping\u001b[38;5;241m=\u001b[39mmapping)\n",
"File \u001b[0;32m/workspaces/pathpyG/src/pathpyG/core/index_map.py:361\u001b[0m, in \u001b[0;36mIndexMap.to_idxs\u001b[0;34m(self, nodes, device)\u001b[0m\n\u001b[1;32m 359\u001b[0m shape \u001b[38;5;241m=\u001b[39m nodes\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_shape \u001b[38;5;241m==\u001b[39m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,):\n\u001b[0;32m--> 361\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid_to_idx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnodes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_to_idx[\u001b[38;5;28mtuple\u001b[39m(node)] \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m nodes\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_shape)], device\u001b[38;5;241m=\u001b[39mdevice)\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m 364\u001b[0m shape[: \u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_shape) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 365\u001b[0m )\n",
"File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/cuda/__init__.py:314\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCUDA_MODULE_LOADING\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron:\n\u001b[1;32m 313\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCUDA_MODULE_LOADING\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLAZY\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 314\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cuda_init\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;66;03m# Some of the queued calls may reentrantly call _lazy_init();\u001b[39;00m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;66;03m# we need to just return without initializing in that case.\u001b[39;00m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;66;03m# However, we must not let any *other* threads in!\u001b[39;00m\n\u001b[1;32m 318\u001b[0m _tls\u001b[38;5;241m.\u001b[39mis_initializing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"\u001b[0;31mRuntimeError\u001b[0m: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 500: named symbol not found"
Expand Down Expand Up @@ -1024,7 +1163,7 @@
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 17,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1052,7 +1191,7 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 18,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1084,7 +1223,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 19,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1118,7 +1257,7 @@
"\n",
"</style>\n",
"\n",
"<div id = \"x7a28caf1b6044e7c89e451a75c0ef22c\"> </div>\n",
"<div id = \"x34cf302f998c48d2a9a314fd81856883\"> </div>\n",
"<script charset=\"utf-8\" src=\"https://d3js.org/d3.v5.min.js\"></script>\n",
"<script charset=\"utf-8\">\n",
"// Load via requireJS if available (jupyter notebook environment)\n",
Expand Down Expand Up @@ -1150,8 +1289,8 @@
" }\n",
"};\n",
"require(['d3'], function(d3){ //START\n",
"const data = {\"edges\": [{\"uid\": \"a-b\", \"source\": \"a\", \"target\": \"b\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"b-c\", \"source\": \"b\", \"target\": \"c\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"c-a\", \"source\": \"c\", \"target\": \"a\", \"weight\": 1, \"color\": \"gray\"}], \"nodes\": [{\"uid\": \"a\", \"label\": \"a\"}, {\"uid\": \"b\", \"label\": \"b\"}, {\"uid\": \"c\", \"label\": \"c\"}]}\n",
"const config = {\"edge_color\": \"gray\", \"node_label\": [\"a\", \"b\", \"c\"], \"directed\": true, \"curved\": true, \"selector\": \"#x7a28caf1b6044e7c89e451a75c0ef22c\"}\n",
"const data = {\"edges\": [{\"uid\": \"a-b\", \"source\": \"a\", \"target\": \"b\", \"color\": \"gray\", \"weight\": 1}, {\"uid\": \"b-c\", \"source\": \"b\", \"target\": \"c\", \"color\": \"gray\", \"weight\": 1}, {\"uid\": \"c-a\", \"source\": \"c\", \"target\": \"a\", \"color\": \"gray\", \"weight\": 1}], \"nodes\": [{\"uid\": \"a\", \"label\": \"a\"}, {\"uid\": \"b\", \"label\": \"b\"}, {\"uid\": \"c\", \"label\": \"c\"}]}\n",
"const config = {\"edge_color\": \"gray\", \"node_label\": [\"a\", \"b\", \"c\"], \"directed\": true, \"curved\": true, \"selector\": \"#x34cf302f998c48d2a9a314fd81856883\"}\n",
"console.log(\"Static Network Template\");\n",
"/* Resources\n",
" https://bl.ocks.org/mapio/53fed7d84cd1812d6a6639ed7aa83868\n",
Expand Down Expand Up @@ -1732,7 +1871,7 @@
},
{
"cell_type": "code",
"execution_count": 77,
"execution_count": 20,
"metadata": {},
"outputs": [
{
Expand All @@ -1759,7 +1898,7 @@
},
{
"cell_type": "code",
"execution_count": 78,
"execution_count": 21,
"metadata": {},
"outputs": [
{
Expand Down
Loading
Loading