visualize_network.py

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)