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""" |
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Basic NetworkX helpers for MedGenesis graphs. |
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Key improvement: |
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build_nx() now accepts edge dictionaries in either of the two |
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common formats: |
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• {'source': 'n1', 'target': 'n2'} (Streamlit-agraph) |
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• {'from': 'n1', 'to': 'n2'} (PyVis) |
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This prevents KeyError crashes when nodes / edges come from different |
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UI toolchains. |
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""" |
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from typing import List, Dict, Tuple |
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import networkx as nx |
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def _edge_endpoints(e: Dict) -> Tuple[str, str] | None: |
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"""Return (src, dst) if both ends exist; otherwise None.""" |
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src = e.get("source") or e.get("from") |
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dst = e.get("target") or e.get("to") |
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if src and dst: |
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return src, dst |
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return None |
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def build_nx(nodes: List[Dict], edges: List[Dict]) -> nx.Graph: |
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""" |
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Convert agraph/PyVis node+edge dicts into a NetworkX Graph. |
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* Skips malformed edges rather than raising KeyError. |
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* Node label stored as attribute 'label'. |
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""" |
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G = nx.Graph() |
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for n in nodes: |
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G.add_node(n["id"], label=n.get("label", n["id"])) |
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for e in edges: |
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endpoints = _edge_endpoints(e) |
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if endpoints: |
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G.add_edge(*endpoints) |
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return G |
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def get_top_hubs(G: nx.Graph, k: int = 5) -> List[Tuple[str, float]]: |
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"""Top-k nodes by degree centrality.""" |
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dc = nx.degree_centrality(G) |
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return sorted(dc.items(), key=lambda x: x[1], reverse=True)[:k] |
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def get_density(G: nx.Graph) -> float: |
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"""Return graph density in [0,1].""" |
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return nx.density(G) |
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