feat(graph): centrality + cluster analytics (corpus graph PR B)

The Obsidian "Graph Analysis" equivalent — surfaces influence and structure
beyond raw citation count.

Backend (new web/graph_metrics.py — pure, dependency-free, no DB → G2):
- PageRank (power-iteration), betweenness (Brandes), community (deterministic
  label-propagation + connected-components fallback), computed in-memory over
  the precedent citation subgraph that build_corpus_graph already fetched.
  Normalized 0–1; community ints dense-ranked by size (stable colours).
- GraphNode += pagerank/betweenness/community (None unless metrics=true).
- build_corpus_graph + /api/graph/corpus gain metrics=false (default path
  unchanged). Validated on the live corpus: 147 nodes in 13ms.

Frontend:
- graph.ts: GraphNode metrics fields + metrics param.
- graph-canvas: color-by (type | practice_area | precedent_level | community |
  recency) and size-by (in-degree | pagerank | betweenness) via colorForNode /
  radiusForNode; exported palettes.
- graph-view: colorBy/sizeBy controls; metrics requested only when needed;
  global metrics overlaid onto neighborhood nodes by id (a node's PageRank
  shouldn't change when focused); a ranking panel (Tabs: המשפיעות / גשרים,
  click → focus); dynamic legend per color-by.
- graph-filter-panel: "צביעה לפי" + "גודל נקודה לפי" Selects.

web-ui build + lint pass. Invariants: G2 (metrics pure, no DB writes),
UI2 (model grows on explicit Pydantic). api:types post-deploy.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

web/graph_api.py

@@ -35,6 +35,8 @@ from uuid import UUID
 import asyncpg
 from pydantic import BaseModel
 
+from web import graph_metrics
+
 # ── Node-type vocabulary ─────────────────────────────────────────────
 VALID_NODE_TYPES = {"precedent", "halacha", "topic", "practice_area"}
 DEFAULT_NODE_TYPES = ("precedent", "topic", "practice_area")
@@ -63,6 +65,10 @@ class GraphNode(BaseModel):
     court: str | None = None  # precedents only — for color-by / filter
     date: str | None = None  # precedents only — ISO date, for recency color/filter
     case_law_id: str | None = None  # canonical id for deep-link (precedents)
+    # Graph metrics — populated only when ``metrics=true`` (precedents only).
+    pagerank: float | None = None  # normalized 0–1 (global influence)
+    betweenness: float | None = None  # normalized 0–1 (bridge-ness)
+    community: int | None = None  # dense cluster id, 0 = largest
 
 
 class GraphFacets(BaseModel):
@@ -243,6 +249,7 @@ async def build_corpus_graph(
     district: str = "",
     year_from: int = 0,
     year_to: int = 0,
+    metrics: bool = False,
 ) -> CorpusGraph:
     """Assemble the full corpus graph under the given filters.
 
@@ -250,6 +257,10 @@ async def build_corpus_graph(
     so clipping never hides the structurally important nodes. ``truncated`` +
     ``total_available`` let the UI prompt the user to narrow filters. All
     filters are applied server-side in the WHERE clause (G5).
+
+    When ``metrics`` is true, PageRank / betweenness / community are computed
+    in-memory over the precedent citation subgraph (``graph_metrics``) and
+    stamped onto precedent nodes — no extra DB work (G2).
     """
     types = normalize_node_types(node_types)
     cap = max(1, min(int(limit), NODE_CAP_MAX))
@@ -298,6 +309,9 @@ async def build_corpus_graph(
         hub_nodes, edges = await _edges_and_hubs(conn, prec_rows, types)
         nodes.extend(hub_nodes)
 
+    if metrics:
+        _stamp_metrics(nodes, edges)
+
     return CorpusGraph(
         nodes=nodes,
         edges=edges,
@@ -306,6 +320,23 @@ async def build_corpus_graph(
     )
 
 
+def _stamp_metrics(nodes: list[GraphNode], edges: list[GraphEdge]) -> None:
+    """Compute PageRank/betweenness/community over the precedent subgraph and
+    stamp them onto precedent nodes in place (hubs stay ``None``)."""
+    prec_ids = [n.id for n in nodes if n.type == "precedent"]
+    if not prec_ids:
+        return
+    directed = [(e.source, e.target) for e in edges if e.type == "cites"]
+    undirected = [(e.source, e.target) for e in edges if e.type == "same_chain"]
+    m = graph_metrics.compute(prec_ids, directed, undirected)
+    for n in nodes:
+        mv = m.get(n.id)
+        if mv:
+            n.pagerank = mv["pagerank"]
+            n.betweenness = mv["betweenness"]
+            n.community = mv["community"]
+
+
 async def build_node_neighborhood(
     pool: asyncpg.Pool,
     node_id: str,