Implement network visualization with Django models and save as HTML

visualize_network.py

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+
+import networkx as nx
+import django
+import os
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "BridgeMentor.settings")
+django.setup()
+
+from core.models import Author, Institution, Concept, AuthorConcept, Affiliation, Work, Topic, Subfield, Field, Domain, AuthorTopic  # noqa: E402
+from pyvis.network import Network  # noqa: E402
+
+G = nx.Graph()
+
+# Add authors
+for a in Author.objects.all()[:100]:
+    G.add_node(a.id, label=a.name, group="Author")
+
+# Add institutions
+for i in Institution.objects.all()[:100]:
+    G.add_node(i.id, label=i.name, group="Institution")
+
+# Add affiliations
+for af in Affiliation.objects.select_related('author', 'institution')[:100]:
+    G.add_edge(af.author.id, af.institution.id, title="affiliated_with")
+
+# Add concepts
+for c in Concept.objects.all()[:100]:
+    G.add_node(c.id, label=c.name, group="Concept")
+
+# AuthorConcept edges
+for ac in AuthorConcept.objects.select_related('author', 'concept')[:100]:
+    G.add_edge(ac.author.id, ac.concept.id, title="interested_in")
+
+# Works and their authors
+for w in Work.objects.select_related('author')[:100]:
+    G.add_node(w.id, label=w.title, group="Work")
+    G.add_edge(w.author.id, w.id, title="authored")
+
+# Topics
+for t in Topic.objects.select_related('subfield')[:100]:
+    G.add_node(t.id, label=t.name, group="Topic")
+    G.add_edge(t.id, t.subfield.id, title="belongs_to")
+
+# AuthorTopics
+for at in AuthorTopic.objects.select_related('author', 'topic')[:100]:
+    G.add_edge(at.author.id, at.topic.id, title="researches")
+
+# Subfields
+for sf in Subfield.objects.select_related('field')[:100]:
+    G.add_node(sf.id, label=sf.name, group="Subfield")
+    G.add_edge(sf.id, sf.field.id, title="part_of")
+
+# Fields
+for f in Field.objects.select_related('domain')[:100]:
+    G.add_node(f.id, label=f.name, group="Field")
+    G.add_edge(f.id, f.domain.id, title="within")
+
+# Domains
+for d in Domain.objects.all()[:100]:
+    G.add_node(d.id, label=d.name, group="Domain")
+
+
+# Generate minimal graph
+net = Network(
+    height="800px",
+    width="100%",
+    bgcolor="#ffffff",
+    font_color="black",
+    heading="Norway Computer Science Research Graph",               # Remove heading
+    # directed=False,          # Simpler view
+    # select_menu=True,   # Enables dropdown to highlight node + neighbors
+    # filter_menu=True    # Enables advanced filtering by attributes
+)
+
+# Reduce interactivity and noise
+net.barnes_hut(gravity=-20000, central_gravity=0.3)
+net.set_options("""
+{
+  "nodes": {
+    "shape": "dot",
+    "size": 10,
+    "font": {
+      "size": 12,
+      "face": "Arial"
+    }
+  },
+  "edges": {
+    "color": {
+      "inherit": true
+    },
+    "smooth": false
+  },
+  "layout": {
+    "improvedLayout": false
+  },
+  "interaction": {
+    "hover": true,
+    "tooltipDelay": 200,
+    "hideEdgesOnDrag": true,
+    "navigationButtons": true,
+    "keyboard": {
+      "enabled": true,
+      "speed": {
+        "x": 10,
+        "y": 10,
+        "zoom": 0.02
+      },
+      "bindToWindow": true
+    }
+  },
+  "physics": {
+    "enabled": true,
+    "barnesHut": {
+      "gravitationalConstant": -20000,
+      "centralGravity": 0.3,
+      "springLength": 95
+    }
+  },
+  "manipulation": {
+    "enabled": false
+  },
+  "configure": {
+    "enabled": false,
+    "filter": ["nodes", "edges", "physics"],
+    "showButton": false
+  }
+}
+""")
+
+net.from_nx(G)
+html_content = net.generate_html()
+
+
+# Save the modified HTML
+html_path = "test.html"
+with open(html_path, "w", encoding="utf-8") as f:
+    f.write(html_content)