Upload folder using huggingface_hub (part 7)

39dbaed verified 3 months ago

7.32 kB

"""
Local Community Detection Algorithms
Local Community Detection (LCD) aims to detected one or a few communities
starting from certain source nodes in the network. This differs from Global
Community Detection (GCD), which aims to partition an entire network into
communities.
LCD is often useful when only a portion of the graph is known or the
graph is large enough that GCD is infeasable
[1]_ Gives a good introduction and overview of LCD
References
----------
.. [1] Baltsou, Georgia, Konstantinos Christopoulos, and Konstantinos Tsichlas.
   Local community detection: A survey. IEEE Access 10 (2022): 110701-110726.
   https://doi.org/10.1109/ACCESS.2022.3213980
"""

__all__ = ["greedy_source_expansion"]


def _clauset_greedy_source_expansion(G, *, source, cutoff=None):
    if cutoff is None:
        cutoff = float("inf")
    C = {source}
    B = {source}
    U = G[source].keys() - C
    T = {frozenset([node, nbr]) for node in B for nbr in G.neighbors(node)}
    I = {edge for edge in T if all(node in C for node in edge)}

    R_value = 0
    while len(C) < cutoff:
        if not U:
            break

        max_R = 0
        best_node = None
        best_node_B = best_node_T = best_node_I = set()

        for v in U:
            R_tmp, B_tmp, T_tmp, I_tmp = _calculate_local_modularity_for_candidate(
                G, v, C, B, T, I
            )
            if R_tmp > max_R:
                max_R = R_tmp
                best_node = v
                best_node_B = B_tmp
                best_node_T = T_tmp
                best_node_I = I_tmp

        C = C | {best_node}
        U.update(G[best_node].keys() - C)
        U.remove(best_node)
        B = best_node_B
        T = best_node_T
        I = best_node_I
        if max_R < R_value:
            break
        R_value = max_R

    return C


def _calculate_local_modularity_for_candidate(G, v, C, B, T, I):
    """
    Compute the local modularity R and updated variables when adding node v to the community.
    Parameters
    ----------
    G : NetworkX graph
        The input graph.
    v : node
        The candidate node to add to the community.
    C : set
        The current set of community nodes.
    B : set
        The current set of boundary nodes.
    T : set of frozenset
        The current set of boundary edges.
    I : set of frozenset
        The current set of internal boundary edges.
    Returns
    -------
    R_tmp : float
        The local modularity after adding node v.
    B_tmp : set
        The updated set of boundary nodes.
    T_tmp : set of frozenset
        The updated set of boundary edges.
    I_tmp : set of frozenset
        The updated set of internal boundary edges.
    """
    C_tmp = C | {v}
    B_tmp = B.copy()
    T_tmp = T.copy()
    I_tmp = I.copy()
    removed_B_nodes = set()

    # Update boundary nodes and edges
    for nbr in G[v]:
        if nbr not in C_tmp:
            # v has nbrs not in the community, so it remains a boundary node
            B_tmp.add(v)
            # Add edge between v and nbr to boundary edges
            T_tmp.add(frozenset([v, nbr]))

        if nbr in B:
            # Check if nbr should be removed from boundary nodes
            # Go through nbrs nbrs to see if it is still a boundary node
            nbr_still_in_B = any(nbr_nbr not in C_tmp for nbr_nbr in G[nbr])
            if not nbr_still_in_B:
                B_tmp.remove(nbr)
                removed_B_nodes.add(nbr)

        if nbr in C_tmp:
            # Add edge between v and nbr to internal edges
            I_tmp.add(frozenset([v, nbr]))

    # Remove edges no longer in the boundary
    for removed_node in removed_B_nodes:
        for removed_node_nbr in G[removed_node]:
            if removed_node_nbr not in B_tmp:
                T_tmp.discard(frozenset([removed_node_nbr, removed_node]))
                I_tmp.discard(frozenset([removed_node_nbr, removed_node]))

    R_tmp = len(I_tmp) / len(T_tmp) if len(T_tmp) > 0 else 1
    return R_tmp, B_tmp, T_tmp, I_tmp


ALGORITHMS = {
    "clauset": _clauset_greedy_source_expansion,
}


def greedy_source_expansion(G, *, source, cutoff=None, method="clauset"):
    r"""Find the local community around a source node.
    Find the local community around a source node using Greedy Source
    Expansion. Greedy Source Expansion generally identifies a local community
    starting from the source node and expands it based on the criteria of the
    chosen algorithm.
    The algorithm is specified with the `method` keyword argument.
    * `"clauset"` [1]_ uses local modularity gain to determine local communities.
        The algorithm adds nbring nodes that maximize local modularity to the
        community iteratively, stopping when no additional nodes improve the modularity
        or when a predefined cutoff is reached.
        Local modularity measures the density of edges within a community relative
        to the total graph. By focusing on local modularity, the algorithm efficiently
        uncovers communities around a specific node without requiring global
        optimization over the entire graph.
        The algorithm assumes that the graph $G$ consists of a known community $C$ and
        an unknown set of nodes $U$, which are adjacent to $C$ . The boundary of the
        community $B$, consists of nodes in $C$ that have at least one nbr in $U$.
        Mathematically, the local modularity is expressed as:
        .. math::
            R = \frac{I}{T}
        where $T$ is the number of edges with one or more endpoints in $B$, and $I$ is the
        number of those edges with neither endpoint in $U$.
    Parameters
    ----------
    G : NetworkX graph
        The input graph.
    source : node
        The source node from which the community expansion begins.
    cutoff : int, optional (default=None)
        The maximum number of nodes to include in the community. If None, the algorithm
        expands until no further modularity gain can be made.
    method : string, optional (default='clauset')
        The algorithm to use to carry out greedy source expansion.
        Supported options: 'clauset'. Other inputs produce a ValueError
    Returns
    -------
    set
        A set of nodes representing the local community around the source node.
    Examples
    --------
    >>> G = nx.karate_club_graph()
    >>> nx.community.greedy_source_expansion(G, source=16)
    {16, 0, 4, 5, 6, 10}
    Notes
    -----
    This algorithm is designed for detecting local communities around a specific node,
    which is useful for large networks where global community detection is computationally
    expensive.
    The result of the algorithm may vary based on the structure of the graph, the choice of
    the source node, and the presence of ties between nodes during the greedy expansion process.
    References
    ----------
    .. [1] Clauset, Aaron. Finding local community structure in networks.
      Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 72, no. 2 (2005): 026132.
      https://arxiv.org/pdf/physics/0503036
    """
    try:
        algo = ALGORITHMS[method]
    except KeyError as e:
        raise ValueError(f"{method} is not a valid choice for an algorithm.") from e

    return algo(G, source=source, cutoff=cutoff)

	"""
	Local Community Detection Algorithms

	Local Community Detection (LCD) aims to detected one or a few communities
	starting from certain source nodes in the network. This differs from Global
	Community Detection (GCD), which aims to partition an entire network into
	communities.

	LCD is often useful when only a portion of the graph is known or the
	graph is large enough that GCD is infeasable

	[1]_ Gives a good introduction and overview of LCD

	References
	----------
	.. [1] Baltsou, Georgia, Konstantinos Christopoulos, and Konstantinos Tsichlas.
	Local community detection: A survey. IEEE Access 10 (2022): 110701-110726.
	https://doi.org/10.1109/ACCESS.2022.3213980


	"""

	__all__ = ["greedy_source_expansion"]


	def _clauset_greedy_source_expansion(G, *, source, cutoff=None):
	if cutoff is None:
	cutoff = float("inf")
	C = {source}
	B = {source}
	U = G[source].keys() - C
	T = {frozenset([node, nbr]) for node in B for nbr in G.neighbors(node)}
	I = {edge for edge in T if all(node in C for node in edge)}

	R_value = 0
	while len(C) < cutoff:
	if not U:
	break

	max_R = 0
	best_node = None
	best_node_B = best_node_T = best_node_I = set()

	for v in U:
	R_tmp, B_tmp, T_tmp, I_tmp = _calculate_local_modularity_for_candidate(
	G, v, C, B, T, I
	)
	if R_tmp > max_R:
	max_R = R_tmp
	best_node = v
	best_node_B = B_tmp
	best_node_T = T_tmp
	best_node_I = I_tmp

	C = C \| {best_node}
	U.update(G[best_node].keys() - C)
	U.remove(best_node)
	B = best_node_B
	T = best_node_T
	I = best_node_I
	if max_R < R_value:
	break
	R_value = max_R

	return C


	def _calculate_local_modularity_for_candidate(G, v, C, B, T, I):
	"""
	Compute the local modularity R and updated variables when adding node v to the community.

	Parameters
	----------
	G : NetworkX graph
	The input graph.
	v : node
	The candidate node to add to the community.
	C : set
	The current set of community nodes.
	B : set
	The current set of boundary nodes.
	T : set of frozenset
	The current set of boundary edges.
	I : set of frozenset
	The current set of internal boundary edges.

	Returns
	-------
	R_tmp : float
	The local modularity after adding node v.
	B_tmp : set
	The updated set of boundary nodes.
	T_tmp : set of frozenset
	The updated set of boundary edges.
	I_tmp : set of frozenset
	The updated set of internal boundary edges.
	"""
	C_tmp = C \| {v}
	B_tmp = B.copy()
	T_tmp = T.copy()
	I_tmp = I.copy()
	removed_B_nodes = set()

	# Update boundary nodes and edges
	for nbr in G[v]:
	if nbr not in C_tmp:
	# v has nbrs not in the community, so it remains a boundary node
	B_tmp.add(v)
	# Add edge between v and nbr to boundary edges
	T_tmp.add(frozenset([v, nbr]))

	if nbr in B:
	# Check if nbr should be removed from boundary nodes
	# Go through nbrs nbrs to see if it is still a boundary node
	nbr_still_in_B = any(nbr_nbr not in C_tmp for nbr_nbr in G[nbr])
	if not nbr_still_in_B:
	B_tmp.remove(nbr)
	removed_B_nodes.add(nbr)

	if nbr in C_tmp:
	# Add edge between v and nbr to internal edges
	I_tmp.add(frozenset([v, nbr]))

	# Remove edges no longer in the boundary
	for removed_node in removed_B_nodes:
	for removed_node_nbr in G[removed_node]:
	if removed_node_nbr not in B_tmp:
	T_tmp.discard(frozenset([removed_node_nbr, removed_node]))
	I_tmp.discard(frozenset([removed_node_nbr, removed_node]))

	R_tmp = len(I_tmp) / len(T_tmp) if len(T_tmp) > 0 else 1
	return R_tmp, B_tmp, T_tmp, I_tmp


	ALGORITHMS = {
	"clauset": _clauset_greedy_source_expansion,
	}


	def greedy_source_expansion(G, *, source, cutoff=None, method="clauset"):
	r"""Find the local community around a source node.

	Find the local community around a source node using Greedy Source
	Expansion. Greedy Source Expansion generally identifies a local community
	starting from the source node and expands it based on the criteria of the
	chosen algorithm.

	The algorithm is specified with the `method` keyword argument.

	* `"clauset"` [1]_ uses local modularity gain to determine local communities.
	The algorithm adds nbring nodes that maximize local modularity to the
	community iteratively, stopping when no additional nodes improve the modularity
	or when a predefined cutoff is reached.

	Local modularity measures the density of edges within a community relative
	to the total graph. By focusing on local modularity, the algorithm efficiently
	uncovers communities around a specific node without requiring global
	optimization over the entire graph.

	The algorithm assumes that the graph $G$ consists of a known community $C$ and
	an unknown set of nodes $U$, which are adjacent to $C$ . The boundary of the
	community $B$, consists of nodes in $C$ that have at least one nbr in $U$.

	Mathematically, the local modularity is expressed as:

	.. math::
	R = \frac{I}{T}

	where $T$ is the number of edges with one or more endpoints in $B$, and $I$ is the
	number of those edges with neither endpoint in $U$.

	Parameters
	----------
	G : NetworkX graph
	The input graph.

	source : node
	The source node from which the community expansion begins.

	cutoff : int, optional (default=None)
	The maximum number of nodes to include in the community. If None, the algorithm
	expands until no further modularity gain can be made.

	method : string, optional (default='clauset')
	The algorithm to use to carry out greedy source expansion.
	Supported options: 'clauset'. Other inputs produce a ValueError

	Returns
	-------
	set
	A set of nodes representing the local community around the source node.

	Examples
	--------
	>>> G = nx.karate_club_graph()
	>>> nx.community.greedy_source_expansion(G, source=16)
	{16, 0, 4, 5, 6, 10}

	Notes
	-----
	This algorithm is designed for detecting local communities around a specific node,
	which is useful for large networks where global community detection is computationally
	expensive.

	The result of the algorithm may vary based on the structure of the graph, the choice of
	the source node, and the presence of ties between nodes during the greedy expansion process.

	References
	----------
	.. [1] Clauset, Aaron. Finding local community structure in networks.
	Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 72, no. 2 (2005): 026132.
	https://arxiv.org/pdf/physics/0503036

	"""
	try:
	algo = ALGORITHMS[method]
	except KeyError as e:
	raise ValueError(f"{method} is not a valid choice for an algorithm.") from e

	return algo(G, source=source, cutoff=cutoff)