Function to_html

graphify/export.py:632–867

Generate an interactive vis.js HTML visualization of the graph. Features: node size by degree, click-to-inspect panel, search box, community filter, physics clustering by community, confidence-styled edges. Raises ValueError if graph exceeds MAX_NODES_FOR_VIZ. If member_counts is p

(
    G: nx.Graph,
    communities: dict[int, list[str]],
    output_path: str,
    community_labels: dict[int, str] | None = None,
    member_counts: dict[int, int] | None = None,
    node_limit: int | None = None,
    learning_overlay: dict | None = None,
)

def to_html(
    G: nx.Graph,
    communities: dict[int, list[str]],
    output_path: str,
    community_labels: dict[int, str] | None = None,
    member_counts: dict[int, int] | None = None,
    node_limit: int | None = None,
    learning_overlay: dict | None = None,
) -> None:
    """Generate an interactive vis.js HTML visualization of the graph.

    Features: node size by degree, click-to-inspect panel, search box,
    community filter, physics clustering by community, confidence-styled edges.
    Raises ValueError if graph exceeds MAX_NODES_FOR_VIZ.

    If member_counts is provided (aggregated community view), node sizes are
    based on community member counts rather than graph degree.

    If node_limit is set and the graph exceeds it, automatically builds an
    aggregated community-level meta-graph instead of raising ValueError.
    """
    limit = node_limit if node_limit is not None else _viz_node_limit()
    if G.number_of_nodes() > limit:
        if node_limit is not None:
            # Build aggregated community meta-graph
            from collections import Counter as _Counter
            import networkx as _nx
            print(f"Graph has {G.number_of_nodes()} nodes (above {limit} limit). Building aggregated community view...")
            node_to_community = {nid: cid for cid, members in communities.items() for nid in members}
            meta = _nx.Graph()
            for cid, members in communities.items():
                meta.add_node(str(cid), label=(community_labels or {}).get(cid, f"Community {cid}"))
            edge_counts = _Counter()
            for u, v in G.edges():
                cu, cv = node_to_community.get(u), node_to_community.get(v)
                if cu is not None and cv is not None and cu != cv:
                    edge_counts[(min(cu, cv), max(cu, cv))] += 1
            for (cu, cv), w in edge_counts.items():
                meta.add_edge(str(cu), str(cv), weight=w,
                              relation=f"{w} cross-community edges", confidence="AGGREGATED")
            if meta.number_of_nodes() <= 1:
                print("Single community - aggregated view not useful. Skipping graph.html.")
                return
            meta_communities = {cid: [str(cid)] for cid in communities}
            mc = {cid: len(members) for cid, members in communities.items()}
            # Remap hyperedges from semantic node IDs to community IDs
            raw_hyperedges = G.graph.get("hyperedges", [])
            if raw_hyperedges:
                remapped = []
                for he in raw_hyperedges:
                    he_members = he.get("nodes", [])
                    comm_ids, seen = [], set()
                    for nid in he_members:
                        c = node_to_community.get(nid)
                        if c is None:
                            continue
                        s = str(c)
                        if s in seen: