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"""
graph_metrics.py · Lightweight NetworkX helpers for MedGenesis
Key features
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• Accepts edge dictionaries in either Streamlit-agraph or PyVis style:
{"source": "n1", "target": "n2"} ← agraph
{"from": "n1", "to": "n2"} ← PyVis
• Silently skips malformed edges (no KeyError).
• Provides three public helpers:
build_nx(nodes, edges) → networkx.Graph
get_top_hubs(G, k=5) → List[(node_id, degree_centrality)]
get_density(G) → float (0–1)
"""
from __future__ import annotations
from typing import List, Dict, Tuple
import networkx as nx
# ────────────────────────────────────────────────────────────────────
# Internal helpers
# ────────────────────────────────────────────────────────────────────
def _edge_ends(e: Dict) -> Tuple[str, str] | None:
"""Return (src, dst) tuple if both ends exist; else None."""
src = e.get("source") or e.get("from")
dst = e.get("target") or e.get("to")
if src and dst:
return src, dst
return None
# ────────────────────────────────────────────────────────────────────
# Public API
# ────────────────────────────────────────────────────────────────────
def build_nx(nodes: List[Dict], edges: List[Dict]) -> nx.Graph:
"""
Convert agraph / PyVis node+edge dicts into a NetworkX Graph.
Nodes: must contain "id" (a unique string)
Edges: accepted shapes → {"source":, "target":} or {"from":, "to":}
"""
G = nx.Graph()
# Add nodes with label attribute (used by Metrics tab)
for n in nodes:
G.add_node(n["id"], label=n.get("label", n["id"]))
# Add edges (skip malformed)
for e in edges:
ends = _edge_ends(e)
if ends:
G.add_edge(*ends)
return G
def get_top_hubs(G: nx.Graph, k: int = 5) -> List[Tuple[str, float]]:
"""
Return top-k nodes by degree-centrality.
Example output: [('TP53', 0.42), ('EGFR', 0.36), ...]
"""
dc = nx.degree_centrality(G)
return sorted(dc.items(), key=lambda x: x[1], reverse=True)[:k]
def get_density(G: nx.Graph) -> float:
"""Graph density in [0, 1]."""
return nx.density(G)
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