chore: import upstream snapshot with attribution

graphify/cluster.py

@@ -0,0 +1,320 @@
+"""Community detection on NetworkX graphs. Uses Leiden (graspologic) if available, falls back to Louvain (networkx). Splits oversized communities. Returns cohesion scores."""
+from __future__ import annotations
+import contextlib
+import inspect
+import io
+import json
+import sys
+import networkx as nx
+
+
+def _suppress_output():
+    """Context manager to suppress stdout/stderr during library calls.
+
+    graspologic's leiden() emits ANSI escape sequences (progress bars,
+    colored warnings) that corrupt PowerShell 5.1's scroll buffer on
+    Windows (see issue #19). Redirecting stdout/stderr to devnull during
+    the call prevents this without losing any graphify output.
+    """
+    return contextlib.redirect_stdout(io.StringIO())
+
+
+def _partition(G: nx.Graph, resolution: float = 1.0) -> dict[str, int]:
+    """Run community detection. Returns {node_id: community_id}.
+
+    Tries Leiden (graspologic) first — best quality.
+    Falls back to Louvain (built into networkx) if graspologic is not installed.
+
+    resolution > 1.0 → more, smaller communities.
+    resolution < 1.0 → fewer, larger communities.
+
+    Output from graspologic is suppressed to prevent ANSI escape codes
+    from corrupting terminal scroll buffers on Windows PowerShell 5.1.
+    """
+    stable = nx.Graph()
+    stable.add_nodes_from(sorted(G.nodes(), key=str))
+    edge_rows = sorted(
+        G.edges(data=True),
+        key=lambda row: (
+            str(row[0]),
+            str(row[1]),
+            json.dumps(row[2], sort_keys=True, ensure_ascii=False, default=str),
+        ),
+    )
+    for src, tgt, attrs in edge_rows:
+        stable.add_edge(src, tgt, **attrs)
+
+    try:
+        from graspologic.partition import leiden
+        lsig = inspect.signature(leiden).parameters
+        kwargs: dict = {}
+        if "random_seed" in lsig:
+            kwargs["random_seed"] = 42
+        if "trials" in lsig:
+            kwargs["trials"] = 1
+        if "resolution" in lsig:
+            kwargs["resolution"] = resolution
+        # Suppress graspologic output to prevent ANSI escape codes from
+        # corrupting PowerShell 5.1 scroll buffer (issue #19)
+        old_stderr = sys.stderr
+        try:
+            sys.stderr = io.StringIO()
+            with _suppress_output():
+                result = leiden(stable, **kwargs)
+        finally:
+            sys.stderr = old_stderr
+        return result
+    except ImportError:
+        pass
+
+    # Fallback: networkx louvain (available since networkx 2.7).
+    # Inspect kwargs to stay compatible across NetworkX versions — max_level
+    # was added in a later release and prevents hangs on large sparse graphs.
+    kwargs: dict = {"seed": 42, "threshold": 1e-4, "resolution": resolution}
+    if "max_level" in inspect.signature(nx.community.louvain_communities).parameters:
+        kwargs["max_level"] = 10
+    communities = nx.community.louvain_communities(stable, **kwargs)
+    return {node: cid for cid, nodes in enumerate(communities) for node in nodes}
+
+
+_MAX_COMMUNITY_FRACTION = 0.25   # communities larger than 25% of graph get split
+_MIN_SPLIT_SIZE = 10             # only split if community has at least this many nodes
+_COHESION_SPLIT_THRESHOLD = 0.05 # re-split communities with cohesion below this
+_COHESION_SPLIT_MIN_SIZE = 50    # only cohesion-split if community has at least this many nodes
+
+
+def label_communities_by_hub(
+    G: nx.Graph, communities: dict[int, list[str]]
+) -> dict[int, str]:
+    """Deterministic, LLM-free community labels: name each community after its
+    highest-degree member — the structural hub — so a report reads ``auth`` /
+    ``log_action`` instead of ``Community 70``. Degree is measured on the full graph
+    ``G``; ties break by node id for run-to-run stability. A community whose members
+    are all absent from ``G`` falls back to ``Community {cid}``.
+
+    Used as the default (no-backend) labeler; an LLM naming pass, when configured,
+    overrides these with richer names.
+    """
+    labels: dict[int, str] = {}
+    for cid, members in communities.items():
+        present = [n for n in members if n in G]
+        if not present:
+            labels[cid] = f"Community {cid}"
+            continue
+        # highest degree wins; ties broken by node id (ascending) for determinism
+        hub = min(present, key=lambda n: (-G.degree(n), str(n)))
+        name = str(G.nodes[hub].get("label") or hub).strip()
+        if name.endswith("()"):
+            name = name[:-2]
+        labels[cid] = name or f"Community {cid}"
+    return labels
+
+
+def community_member_sigs(communities: dict[int, list[str]]) -> dict[int, str]:
+    """Per-community membership fingerprints: ``{cid: sha256(sorted member ids)}``.
+
+    Persisted next to ``.graphify_labels.json`` so a later ``cluster-only`` can tell
+    which communities actually changed since labeling. A cid whose members no longer
+    hash the same is a different community — reusing its old (LLM) label there is the
+    "stale label after re-scoping" bug this guards against. Deterministic; independent
+    of cid index, node order, and machine.
+    """
+    import hashlib
+
+    sigs: dict[int, str] = {}
+    for cid, members in communities.items():
+        h = hashlib.sha256()
+        for nid in sorted(str(n) for n in members):
+            h.update(nid.encode("utf-8", "replace"))
+            h.update(b"\x00")
+        sigs[cid] = h.hexdigest()[:16]
+    return sigs
+
+
+def cluster(
+    G: nx.Graph,
+    resolution: float = 1.0,
+    exclude_hubs_percentile: float | None = None,
+) -> dict[int, list[str]]:
+    """Run Leiden community detection. Returns {community_id: [node_ids]}.
+
+    Community IDs are stable across runs: 0 = largest community after splitting.
+    Oversized communities (> 25% of graph nodes, min 10) are split by running
+    a second Leiden pass on the subgraph.
+
+    Accepts directed or undirected graphs. DiGraphs are converted to undirected
+    internally since Louvain/Leiden require undirected input.
+
+    resolution: passed to Leiden/Louvain. >1.0 = more smaller communities,
+        <1.0 = fewer larger communities. Default 1.0.
+    exclude_hubs_percentile: if set (0-100), nodes whose degree exceeds this
+        percentile are excluded from partitioning and reattached to their
+        majority-vote neighbour community afterwards. Useful for staging/utility
+        super-hubs that inflate god-node rankings (#919).
+    """
+    if G.number_of_nodes() == 0:
+        return {}
+    if G.is_directed():
+        G = G.to_undirected()
+    if G.number_of_edges() == 0:
+        return {i: [n] for i, n in enumerate(sorted(G.nodes))}
+
+    # Compute hub exclusion set before removing anything so degree is based on full graph
+    hub_nodes: set[str] = set()
+    if exclude_hubs_percentile is not None:
+        degrees = sorted(d for _, d in G.degree())
+        if degrees:
+            idx = max(0, int(len(degrees) * exclude_hubs_percentile / 100) - 1)
+            threshold = degrees[idx]
+            hub_nodes = {n for n, d in G.degree() if d > threshold}
+
+    # Leiden warns and drops isolates - handle them separately
+    # Also exclude hub nodes from partitioning so they don't pull unrelated
+    # subsystems into the same community
+    excluded = hub_nodes
+    isolates = [n for n in G.nodes() if G.degree(n) == 0 and n not in excluded]
+    connected_nodes = [n for n in G.nodes() if G.degree(n) > 0 and n not in excluded]
+    connected = G.subgraph(connected_nodes)
+
+    raw: dict[int, list[str]] = {}
+    if connected.number_of_nodes() > 0:
+        partition = _partition(connected, resolution=resolution)
+        for node, cid in partition.items():
+            raw.setdefault(cid, []).append(node)
+
+    # Each isolate becomes its own single-node community
+    next_cid = max(raw.keys(), default=-1) + 1
+    for node in isolates:
+        raw[next_cid] = [node]
+        next_cid += 1
+
+    # Reattach excluded hubs by majority-vote neighbour community
+    if hub_nodes:
+        node_community: dict[str, int] = {n: cid for cid, nodes in raw.items() for n in nodes}
+        for hub in sorted(hub_nodes):
+            votes: dict[int, int] = {}
+            for nb in G.neighbors(hub):
+                cid = node_community.get(nb)
+                if cid is not None:
+                    votes[cid] = votes.get(cid, 0) + 1
+            if votes:
+                best = min(votes, key=lambda c: (-votes[c], c))
+                raw.setdefault(best, []).append(hub)
+                node_community[hub] = best
+            else:
+                raw[next_cid] = [hub]
+                node_community[hub] = next_cid
+                next_cid += 1
+
+    # Split oversized communities
+    max_size = max(_MIN_SPLIT_SIZE, int(G.number_of_nodes() * _MAX_COMMUNITY_FRACTION))
+    final_communities: list[list[str]] = []
+    for nodes in raw.values():
+        if len(nodes) > max_size:
+            final_communities.extend(_split_community(G, nodes))
+        else:
+            final_communities.append(nodes)
+
+    # Second pass: re-split low-cohesion communities caused by doc-hub nodes
+    # that bridge otherwise-unrelated subsystems (e.g. CLAUDE.md connected to everything).
+    second_pass: list[list[str]] = []
+    for nodes in final_communities:
+        if len(nodes) >= _COHESION_SPLIT_MIN_SIZE and cohesion_score(G, nodes) < _COHESION_SPLIT_THRESHOLD:
+            splits = _split_community(G, nodes)
+            second_pass.extend(splits if len(splits) > 1 else [nodes])
+        else:
+            second_pass.append(nodes)
+    final_communities = second_pass
+
+    # Re-index by size descending. The tuple(sorted(nodes)) tiebreak makes this a
+    # TOTAL order, so an identical grouping always gets identical community IDs.
+    # Without it, the hundreds of equal-sized small communities are ordered by the
+    # partitioner's (not seed-stable) enumeration order, so their integer IDs
+    # permute run-to-run - which reads as massive "community churn" in a per-node
+    # cid diff even though the actual grouping is reproducible (#1090 follow-up).
+    final_communities.sort(key=lambda nodes: (-len(nodes), tuple(sorted(map(str, nodes)))))
+    return {i: sorted(nodes) for i, nodes in enumerate(final_communities)}
+
+
+def _split_community(G: nx.Graph, nodes: list[str]) -> list[list[str]]:
+    """Run a second Leiden pass on a community subgraph to split it further."""
+    subgraph = G.subgraph(nodes)
+    if subgraph.number_of_edges() == 0:
+        # No edges - split into individual nodes
+        return [[n] for n in sorted(nodes)]
+    try:
+        sub_partition = _partition(subgraph)
+        sub_communities: dict[int, list[str]] = {}
+        for node, cid in sub_partition.items():
+            sub_communities.setdefault(cid, []).append(node)
+        if len(sub_communities) <= 1:
+            return [sorted(nodes)]
+        return [sorted(v) for v in sub_communities.values()]
+    except Exception:
+        return [sorted(nodes)]
+
+
+def cohesion_score(G: nx.Graph, community_nodes: list[str]) -> float:
+    """Ratio of actual intra-community edges to maximum possible."""
+    n = len(community_nodes)
+    if n <= 1:
+        return 1.0
+    subgraph = G.subgraph(community_nodes)
+    actual = subgraph.number_of_edges()
+    possible = n * (n - 1) / 2
+    return actual / possible if possible > 0 else 0.0
+
+
+def score_all(G: nx.Graph, communities: dict[int, list[str]]) -> dict[int, float]:
+    return {cid: cohesion_score(G, nodes) for cid, nodes in communities.items()}
+
+
+def remap_communities_to_previous(
+    communities: dict[int, list[str]],
+    previous_node_community: dict[str, int],
+) -> dict[int, list[str]]:
+    """Remap community IDs to maximize overlap with a previous assignment.
+
+    Uses greedy one-to-one matching by intersection size, then assigns fresh IDs
+    to unmatched communities in deterministic order (size desc, lexical tie-break).
+    """
+    if not communities:
+        return {}
+
+    new_sets = {cid: set(nodes) for cid, nodes in communities.items()}
+    old_sets: dict[int, set[str]] = {}
+    for node, old_cid in previous_node_community.items():
+        old_sets.setdefault(old_cid, set()).add(node)
+
+    overlaps: list[tuple[int, int, int]] = []
+    for old_cid, old_nodes in old_sets.items():
+        for new_cid, new_nodes in new_sets.items():
+            overlap = len(old_nodes & new_nodes)
+            if overlap > 0:
+                overlaps.append((overlap, old_cid, new_cid))
+    overlaps.sort(key=lambda x: (-x[0], x[1], x[2]))
+
+    new_to_final: dict[int, int] = {}
+    used_old_ids: set[int] = set()
+    matched_new_ids: set[int] = set()
+    for _overlap, old_cid, new_cid in overlaps:
+        if old_cid in used_old_ids or new_cid in matched_new_ids:
+            continue
+        new_to_final[new_cid] = old_cid
+        used_old_ids.add(old_cid)
+        matched_new_ids.add(new_cid)
+
+    unmatched = [cid for cid in communities if cid not in matched_new_ids]
+    unmatched.sort(key=lambda cid: (-len(communities[cid]), tuple(sorted(communities[cid]))))
+    next_id = 0
+    for new_cid in unmatched:
+        while next_id in used_old_ids:
+            next_id += 1
+        new_to_final[new_cid] = next_id
+        used_old_ids.add(next_id)
+        next_id += 1
+
+    remapped: dict[int, list[str]] = {}
+    for new_cid, nodes in communities.items():
+        remapped[new_to_final[new_cid]] = sorted(nodes)
+    return dict(sorted(remapped.items(), key=lambda kv: kv[0]))