[WIP] Added functionality of parallel maximal independent set

nx_parallel/algorithms/__init__.py

@@ -15,3 +15,4 @@
 from .cluster import *
 from .link_prediction import *
 from .dag import *
+from .mis import *

nx_parallel/algorithms/mis.py

@@ -0,0 +1,194 @@
+import inspect
+from joblib import Parallel, delayed
+import nx_parallel as nxp
+import networkx as nx
+
+__all__ = ["maximal_independent_set"]
+
+# Import the actual NetworkX implementation (fully unwrapped, not the dispatcher)
+from networkx.algorithms.mis import maximal_independent_set as _nx_mis_dispatcher
+_nx_mis = inspect.unwrap(_nx_mis_dispatcher)
+
+
+@nxp._configure_if_nx_active(should_run=nxp.should_run_if_large(50000))
+def maximal_independent_set(G, nodes=None, seed=None, get_chunks="chunks"):
+    """Returns a random maximal independent set guaranteed to contain
+    a given set of nodes.
+
+    This parallel implementation processes nodes in chunks across multiple
+    cores, using a Luby-style randomized parallel algorithm for speedup
+    on large graphs.
+
+    An independent set is a set of nodes such that the subgraph
+    of G induced by these nodes contains no edges. A maximal
+    independent set is an independent set such that it is not possible
+    to add a new node and still get an independent set.
+
+    The parallel computation divides nodes into chunks and processes them
+    in parallel, iteratively building the independent set faster than
+    sequential processing on large graphs.
+
+    networkx.maximal_independent_set: https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.mis.maximal_independent_set.html
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    nodes : list or iterable, optional
+        Nodes that must be part of the independent set. This set of nodes
+        must be independent. If not provided, a random starting node is chosen.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    get_chunks : str, function (default = "chunks")
+        A function that takes in a list of nodes and returns chunks.
+        The default chunking divides nodes into n_jobs chunks.
+
+    Returns
+    -------
+    indep_nodes : list
+        List of nodes that are part of a maximal independent set.
+
+    Raises
+    ------
+    NetworkXUnfeasible
+        If the nodes in the provided list are not part of the graph or
+        do not form an independent set, an exception is raised.
+
+    NetworkXNotImplemented
+        If `G` is directed.
+
+    Examples
+    --------
+    >>> import networkx as nx
+    >>> import nx_parallel as nxp
+    >>> G = nx.path_graph(5)
+    >>> nxp.maximal_independent_set(G)  # doctest: +SKIP
+    [4, 0, 2]
+    >>> nxp.maximal_independent_set(G, [1])  # doctest: +SKIP
+    [1, 3]
+
+    Notes
+    -----
+    This algorithm does not solve the maximum independent set problem.
+    The parallel version uses a chunk-based parallel algorithm that
+    provides speedup on large graphs (>= 50000 nodes). For smaller graphs,
+    the NetworkX sequential version is used automatically.
+
+    """
+    if hasattr(G, "graph_object"):
+        G = G.graph_object
+
+    # Validate directed graph
+    if G.is_directed():
+        raise nx.NetworkXNotImplemented("Not implemented for directed graphs.")
+
+    # Convert seed to Random object if needed (for fallback and parallel execution)
+    import random
+    if seed is not None:
+        if hasattr(seed, 'random'):
+            # It's already a RandomState/Random object
+            rng = seed
+        else:
+            # It's a seed value
+            rng = random.Random(seed)
+    else:
+        rng = random.Random()
+
+    # Check if we should run parallel version
+    # This is needed when backend is explicitly specified
+    should_run_result = maximal_independent_set.should_run(G, nodes, seed)
+    if should_run_result is not True:
+        # Fall back to NetworkX sequential (unwrapped version needs Random object)
+        return _nx_mis(G, nodes=nodes, seed=rng)
+
+    # Validate nodes parameter
+    if nodes is not None:
+        nodes_set = set(nodes)
+        if not nodes_set.issubset(G):
+            raise nx.NetworkXUnfeasible(f"{nodes} is not a subset of the nodes of G")
+        neighbors = set.union(*[set(G.adj[v]) for v in nodes_set]) if nodes_set else set()
+        if set.intersection(neighbors, nodes_set):
+            raise nx.NetworkXUnfeasible(f"{nodes} is not an independent set of G")
+    else:
+        nodes_set = set()
+
+    n_jobs = nxp.get_n_jobs()
+
+    # Parallel strategy: Run complete MIS algorithm on node chunks independently
+    # Then merge results by resolving conflicts
+    all_nodes = list(G.nodes())
+
+    # Remove required nodes and their neighbors from consideration
+    if nodes_set:
+        available = set(all_nodes) - nodes_set
+        for node in nodes_set:
+            available.discard(node)
+            available.difference_update(G.neighbors(node))
+        available = list(available)
+    else:
+        available = all_nodes
+
+    # Shuffle for randomness
+    rng.shuffle(available)
+
+    # Split into chunks
+    if get_chunks == "chunks":
+        chunks = list(nxp.chunks(available, n_jobs))
+    else:
+        chunks = list(get_chunks(available))
+
+    # Precompute adjacency
+    adj_dict = {node: set(G.neighbors(node)) for node in G.nodes()}
+
+    def _process_chunk_independent(chunk, chunk_seed):
+        """Process chunk completely independently - build local MIS."""
+        local_rng = random.Random(chunk_seed)
+        local_mis = []
+        local_excluded = set()
+
+        # Shuffle chunk for randomness
+        chunk_list = list(chunk)
+        local_rng.shuffle(chunk_list)
+
+        for node in chunk_list:
+            if node not in local_excluded:
+                # Add to MIS
+                local_mis.append(node)
+                local_excluded.add(node)
+                # Mark neighbors as excluded (only within this chunk)
+                for neighbor in adj_dict[node]:
+                    if neighbor in chunk_list:
+                        local_excluded.add(neighbor)
+
+        return local_mis
+
+    # Generate seeds for each chunk
+    chunk_seeds = [rng.randint(0, 2**31 - 1) for _ in range(len(chunks))]
+
+    # Process chunks in parallel
+    results = Parallel()(
+        delayed(_process_chunk_independent)(chunk, chunk_seeds[i])
+        for i, chunk in enumerate(chunks)
+    )
+
+    # Merge results: resolve conflicts between chunks
+    indep_set = list(nodes_set) if nodes_set else []
+    excluded = set(nodes_set)
+
+    if nodes_set:
+        for node in nodes_set:
+            excluded.update(adj_dict[node])
+
+    # Process results in order, greedily adding non-conflicting nodes
+    for local_mis in results:
+        for node in local_mis:
+            if node not in excluded:
+                indep_set.append(node)
+                excluded.add(node)
+                excluded.update(adj_dict[node])
+
+    return indep_set

nx_parallel/algorithms/tests/test_mis.py

@@ -0,0 +1,125 @@
+import networkx as nx
+import nx_parallel as nxp
+import pytest
+
+
+def test_maximal_independent_set_basic():
+    G = nx.path_graph(5)
+    H = nxp.ParallelGraph(G)
+    result = nxp.maximal_independent_set(H)
+
+    result_set = set(result)
+    for node in result:
+        neighbors = set(G.neighbors(node))
+        assert not result_set.intersection(neighbors)
+
+    for node in G.nodes():
+        if node not in result_set:
+            neighbors = set(G.neighbors(node))
+            assert result_set.intersection(neighbors)
+
+
+def test_maximal_independent_set_with_required_nodes():
+    G = nx.path_graph(7)
+    H = nxp.ParallelGraph(G)
+    required_nodes = [1, 3]
+    result = nxp.maximal_independent_set(H, nodes=required_nodes)
+
+    assert 1 in result
+    assert 3 in result
+
+    result_set = set(result)
+    for node in result:
+        neighbors = set(G.neighbors(node))
+        assert not result_set.intersection(neighbors)
+
+
+def test_maximal_independent_set_invalid_nodes():
+    G = nx.path_graph(5)
+    H = nxp.ParallelGraph(G)
+
+    with pytest.raises(nx.NetworkXUnfeasible):
+        nxp.maximal_independent_set(H, nodes=[10, 20])
+
+    with pytest.raises(nx.NetworkXUnfeasible):
+        nxp.maximal_independent_set(H, nodes=[0, 1])
+
+
+def test_maximal_independent_set_directed_graph():
+    G = nx.DiGraph([(0, 1), (1, 2)])
+    H = nxp.ParallelGraph(G)
+
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nxp.maximal_independent_set(H)
+
+
+def test_maximal_independent_set_deterministic_with_seed():
+    G = nx.karate_club_graph()
+    H = nxp.ParallelGraph(G)
+
+    result1 = nxp.maximal_independent_set(H, seed=42)
+    result2 = nxp.maximal_independent_set(H, seed=42)
+
+    assert result1 == result2
+
+
+def test_maximal_independent_set_different_seeds():
+    G = nx.karate_club_graph()
+    H = nxp.ParallelGraph(G)
+
+    result1 = nxp.maximal_independent_set(H, seed=42)
+    result2 = nxp.maximal_independent_set(H, seed=100)
+
+    for result in [result1, result2]:
+        result_set = set(result)
+        for node in result:
+            neighbors = set(G.neighbors(node))
+            assert not result_set.intersection(neighbors)
+
+
+def test_maximal_independent_set_complete_graph():
+    G = nx.complete_graph(5)
+    H = nxp.ParallelGraph(G)
+    result = nxp.maximal_independent_set(H)
+
+    assert len(result) == 1
+
+
+def test_maximal_independent_set_empty_graph():
+    G = nx.empty_graph(5)
+    H = nxp.ParallelGraph(G)
+    result = nxp.maximal_independent_set(H)
+
+    assert len(result) == 5
+
+
+def test_maximal_independent_set_large_graph():
+    G = nx.fast_gnp_random_graph(150, 0.1, seed=42)
+    H = nxp.ParallelGraph(G)
+    result = nxp.maximal_independent_set(H, seed=42)
+
+    result_set = set(result)
+    for node in result:
+        neighbors = set(G.neighbors(node))
+        assert not result_set.intersection(neighbors)
+
+    for node in G.nodes():
+        if node not in result_set:
+            neighbors = set(G.neighbors(node))
+            assert result_set.intersection(neighbors)
+
+
+def test_maximal_independent_set_random_graph():
+    G = nx.fast_gnp_random_graph(50, 0.1, seed=42)
+    H = nxp.ParallelGraph(G)
+    result = nxp.maximal_independent_set(H, seed=42)
+
+    result_set = set(result)
+    for node in result:
+        neighbors = set(G.neighbors(node))
+        assert not result_set.intersection(neighbors)
+
+    for node in G.nodes():
+        if node not in result_set:
+            neighbors = set(G.neighbors(node))
+            assert result_set.intersection(neighbors)

nx_parallel/interface.py

@@ -63,6 +63,8 @@
     "average_neighbor_degree",
     # Connectivity
     "all_pairs_node_connectivity",
+    # Maximal Independent Set
+    "maximal_independent_set",
 ]
 
 

nx_parallel/tests/test_get_chunks.py

@@ -38,6 +38,7 @@ def test_get_functions_with_get_chunks():
 ignore_funcs = [
     "number_of_isolates",
     "is_reachable",
+    "maximal_independent_set",
 ]
 
 

nx_parallel/utils/should_run_policies.py

@@ -9,20 +9,39 @@
 ]
 
 
-def should_skip_parallel(*_):
+def should_skip_parallel(*_, **__):
     return "Fast algorithm; skip parallel execution"
 
 
-def should_run_if_large(G, *_):
-    if hasattr(G, "graph_object"):
-        G = G.graph_object
+def should_run_if_large(nodes_threshold=200, *_, **__):
+    # If nodes_threshold is a graph-like object, it's being used as a direct should_run
+    # function instead of a factory. Use default threshold.
+    if hasattr(nodes_threshold, '__len__') and hasattr(nodes_threshold, 'nodes'):
+        # nodes_threshold is actually a graph, use it as G with default threshold
+        G = nodes_threshold
+        threshold = 200
 
-    if len(G) <= 200:
-        return "Graph too small for parallel execution"
-    return True
+        if hasattr(G, "graph_object"):
+            G = G.graph_object
+
+        if len(G) < threshold:
+            return "Graph too small for parallel execution"
+        return True
+
+    # Otherwise, it's being used as a factory, return a wrapper
+    threshold = nodes_threshold
+    def wrapper(G, *_, **__):
+        if hasattr(G, "graph_object"):
+            G = G.graph_object
+
+        if len(G) < threshold:
+            return "Graph too small for parallel execution"
+        return True
+
+    return wrapper
 
 
-def default_should_run(*_):
+def default_should_run(*_, **__):
     n_jobs = nxp.get_n_jobs()
     print(f"{n_jobs=}")
     if n_jobs in (None, 0, 1):
@@ -31,7 +50,7 @@ def default_should_run(*_):
 
 
 def should_run_if_sparse(threshold=0.3):
-    def wrapper(G, *_):
+    def wrapper(G, *_, **__):
         if hasattr(G, "graph_object"):
             G = G.graph_object