A few more algorithms and automatically add algorithms to README

.github/workflows/lint.yml

@@ -1,10 +1,12 @@
+# Rely on pre-commit.ci instead
 name: Lint via pre-commit
 
 on:
-  pull_request:
-  push:
-    branches-ignore:
-      - main
+  workflow_dispatch:
+  # pull_request:
+  # push:
+  #   branches-ignore:
+  #     - main
 
 permissions:
   contents: read

.pre-commit-config.yaml

@@ -25,7 +25,7 @@ repos:
       - id: mixed-line-ending
       - id: trailing-whitespace
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.12.2
+    rev: v0.13
     hooks:
       - id: validate-pyproject
         name: Validate pyproject.toml
@@ -40,7 +40,7 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.2
+    rev: v3.4.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -55,7 +55,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.264
+    rev: v0.0.269
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]
@@ -66,7 +66,7 @@ repos:
         additional_dependencies: &flake8_dependencies
         # These versions need updated manually
         - flake8==6.0.0
-        - flake8-bugbear==23.3.23
+        - flake8-bugbear==23.5.9
         - flake8-simplify==0.20.0
   - repo: https://github.com/asottile/yesqa
     rev: v1.4.0
@@ -81,7 +81,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(graphblas_algorithms|docs)/
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.264
+    rev: v0.0.269
     hooks:
       - id: ruff
   # `pyroma` may help keep our package standards up to date if best practices change.

README.md

@@ -112,93 +112,138 @@ The following NetworkX algorithms have been implemented
 by graphblas-algorithms and can be used following the
 dispatch pattern shown above.
 
-- Boundary
-  - edge_boundary
-  - node_boundary
-- Centrality
-  - degree_centrality
-  - eigenvector_centrality
-  - in_degree_centrality
-  - katz_centrality
-  - out_degree_centrality
-- Cluster
-  - average_clustering
-  - clustering
-  - generalized_degree
-  - square_clustering
-  - transitivity
-  - triangles
-- Community
-  - inter_community_edges
-  - intra_community_edges
-- Components
-  - is_connected
-  - is_weakly_connected
-  - node_connected_component
-- Core
-  - k_truss
-- Cuts
-  - boundary_expansion
-  - conductance
-  - cut_size
-  - edge_expansion
-  - mixing_expansion
-  - node_expansion
-  - normalized_cut_size
-  - volume
-- DAG
-  - ancestors
-  - descendants
-- Dominating
-  - is_dominating_set
-- Generators
-  - ego_graph
-- Isolate
-  - is_isolate
-  - isolates
-  - number_of_isolates
-- Link Analysis
-  - google_matrix
-  - hits
-  - pagerank
-- Operators
-  - compose
-  - difference
-  - disjoint_union
-  - full_join
-  - intersection
-  - symmetric_difference
-  - union
-- Reciprocity
-  - overall_reciprocity
-  - reciprocity
-- Regular
-  - is_k_regular
-  - is_regular
-- Shortest Paths
-  - all_pairs_bellman_ford_path_length
-  - all_pairs_shortest_path_length
-  - bellman_ford_path
-  - floyd_warshall
-  - floyd_warshall_numpy
-  - floyd_warshall_predecessor_and_distance
-  - has_path
-  - negative_edge_cycle
-  - single_source_bellman_ford_path_length
-  - single_source_shortest_path_length
-  - single_target_shortest_path_length
-- Simple Paths
-  - is_simple_path
-- S Metric
-  - s_metric
-- Structural Holes
-  - mutual_weight
-- Tournament
-  - is_tournament
-  - score_sequence
-  - tournament_matrix
-- Traversal
-  - bfs_layers
-  - descendants_at_distance
-- Triads
-  - is_triad
+[//]: # (Begin auto-generated code)
+
+```
+graphblas_algorithms.nxapi
+├── boundary
+│   ├── edge_boundary
+│   └── node_boundary
+├── centrality
+│   ├── degree_alg
+│   │   ├── degree_centrality
+│   │   ├── in_degree_centrality
+│   │   └── out_degree_centrality
+│   ├── eigenvector
+│   │   └── eigenvector_centrality
+│   └── katz
+│       └── katz_centrality
+├── cluster
+│   ├── average_clustering
+│   ├── clustering
+│   ├── generalized_degree
+│   ├── square_clustering
+│   ├── transitivity
+│   └── triangles
+├── community
+│   └── quality
+│       ├── inter_community_edges
+│       └── intra_community_edges
+├── components
+│   ├── connected
+│   │   ├── is_connected
+│   │   └── node_connected_component
+│   └── weakly_connected
+│       └── is_weakly_connected
+├── core
+│   └── k_truss
+├── cuts
+│   ├── boundary_expansion
+│   ├── conductance
+│   ├── cut_size
+│   ├── edge_expansion
+│   ├── mixing_expansion
+│   ├── node_expansion
+│   ├── normalized_cut_size
+│   └── volume
+├── dag
+│   ├── ancestors
+│   └── descendants
+├── dominating
+│   └── is_dominating_set
+├── efficiency_measures
+│   └── efficiency
+├── generators
+│   └── ego
+│       └── ego_graph
+├── isolate
+│   ├── is_isolate
+│   ├── isolates
+│   └── number_of_isolates
+├── isomorphism
+│   └── isomorph
+│       ├── fast_could_be_isomorphic
+│       └── faster_could_be_isomorphic
+├── linalg
+│   ├── bethehessianmatrix
+│   │   └── bethe_hessian_matrix
+│   ├── graphmatrix
+│   │   └── adjacency_matrix
+│   ├── laplacianmatrix
+│   │   ├── laplacian_matrix
+│   │   └── normalized_laplacian_matrix
+│   └── modularitymatrix
+│       ├── directed_modularity_matrix
+│       └── modularity_matrix
+├── link_analysis
+│   ├── hits_alg
+│   │   └── hits
+│   └── pagerank_alg
+│       ├── google_matrix
+│       └── pagerank
+├── lowest_common_ancestors
+│   └── lowest_common_ancestor
+├── operators
+│   ├── binary
+│   │   ├── compose
+│   │   ├── difference
+│   │   ├── disjoint_union
+│   │   ├── full_join
+│   │   ├── intersection
+│   │   ├── symmetric_difference
+│   │   └── union
+│   └── unary
+│       ├── complement
+│       └── reverse
+├── reciprocity
+│   ├── overall_reciprocity
+│   └── reciprocity
+├── regular
+│   ├── is_k_regular
+│   └── is_regular
+├── shortest_paths
+│   ├── dense
+│   │   ├── floyd_warshall
+│   │   ├── floyd_warshall_numpy
+│   │   └── floyd_warshall_predecessor_and_distance
+│   ├── generic
+│   │   └── has_path
+│   ├── unweighted
+│   │   ├── all_pairs_shortest_path_length
+│   │   ├── single_source_shortest_path_length
+│   │   └── single_target_shortest_path_length
+│   └── weighted
+│       ├── all_pairs_bellman_ford_path_length
+│       ├── bellman_ford_path
+│       ├── bellman_ford_path_length
+│       ├── negative_edge_cycle
+│       └── single_source_bellman_ford_path_length
+├── simple_paths
+│   └── is_simple_path
+├── smetric
+│   └── s_metric
+├── structuralholes
+│   └── mutual_weight
+├── tournament
+│   ├── is_tournament
+│   ├── score_sequence
+│   └── tournament_matrix
+├── traversal
+│   └── breadth_first_search
+│       ├── bfs_layers
+│       └── descendants_at_distance
+└── triads
+    └── is_triad
+```
+
+[//]: # (End auto-generated code)

environment.yml

@@ -42,6 +42,8 @@ dependencies:
     - pydot
     - pygraphviz
     - sympy
+    # For updating algorithm list in README
+    - rich
     # For linting
     - pre-commit
     # For testing

graphblas_algorithms/__init__.py

@@ -1,9 +1,10 @@
 import importlib.metadata
 
 from .classes import *
+from .generators import *
+from .linalg import *
 
 from .algorithms import *  # isort:skip
-from .generators import *  # isort:skip
 
 try:
     __version__ = importlib.metadata.version("graphblas-algorithms")

graphblas_algorithms/algorithms/__init__.py

@@ -8,8 +8,11 @@
 from .cuts import *
 from .dag import *
 from .dominating import *
+from .efficiency_measures import *
 from .isolate import *
+from .isomorphism import *
 from .link_analysis import *
+from .lowest_common_ancestors import *
 from .operators import *
 from .reciprocity import *
 from .regular import *

graphblas_algorithms/algorithms/_bfs.py

@@ -11,16 +11,25 @@ def _get_cutoff(n, cutoff):
     return cutoff + 1  # Inclusive
 
 
-def _bfs_plain(G, source=None, target=None, *, index=None, cutoff=None):
+# Push-pull optimization is possible, but annoying to implement
+def _bfs_plain(
+    G, source=None, target=None, *, index=None, cutoff=None, transpose=False, name="bfs_plain"
+):
     if source is not None:
+        if source not in G._key_to_id:
+            raise KeyError(f"The node {source} is not in the graph")
         index = G._key_to_id[source]
     if target is not None:
+        if target not in G._key_to_id:
+            raise KeyError(f"The node {target} is not in the graph")
         dst_id = G._key_to_id[target]
     else:
         dst_id = None
     A = G.get_property("offdiag")
+    if transpose and G.is_directed():
+        A = A.T  # TODO: should we use "AT" instead?
     n = A.nrows
-    v = Vector(bool, n, name="bfs_plain")
+    v = Vector(bool, n, name=name)
     q = Vector(bool, n, name="q")
     v[index] = True
     q[index] = True
@@ -30,16 +39,22 @@ def _bfs_plain(G, source=None, target=None, *, index=None, cutoff=None):
         q(~v.S, replace) << any_pair_bool(q @ A)
         if q.nvals == 0:
             break
+        v(q.S) << True
         if dst_id is not None and dst_id in q:
             break
-        v(q.S) << True
     return v
 
 
-def _bfs_level(G, source, cutoff=None, *, transpose=False, dtype=int):
+def _bfs_level(G, source, target=None, *, cutoff=None, transpose=False, dtype=int):
     if dtype == bool:
         dtype = int
     index = G._key_to_id[source]
+    if target is not None:
+        if target not in G._key_to_id:
+            raise KeyError(f"The node {target} is not in the graph")
+        dst_id = G._key_to_id[target]
+    else:
+        dst_id = None
     A = G.get_property("offdiag")
     if transpose and G.is_directed():
         A = A.T  # TODO: should we use "AT" instead?
@@ -55,10 +70,12 @@ def _bfs_level(G, source, cutoff=None, *, transpose=False, dtype=int):
         if q.nvals == 0:
             break
         v(q.S) << i
+        if dst_id is not None and dst_id in q:
+            break
     return v
 
 
-def _bfs_levels(G, nodes, cutoff=None, *, dtype=int):
+def _bfs_levels(G, nodes, *, cutoff=None, dtype=int):
     if dtype == bool:
         dtype = int
     A = G.get_property("offdiag")
@@ -90,7 +107,7 @@ def _bfs_levels(G, nodes, cutoff=None, *, dtype=int):
     return D
 
 
-def _bfs_parent(G, source, cutoff=None, *, target=None, transpose=False, dtype=int):
+def _bfs_parent(G, source, target=None, *, cutoff=None, transpose=False, dtype=int):
     if dtype == bool:
         dtype = int
     index = G._key_to_id[source]