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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_measures.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_measures.py new file mode 100644 index 00000000..8e15bf8d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_measures.py @@ -0,0 +1,1022 @@ +"""Graph diameter, radius, eccentricity and other properties.""" + +import math + +import networkx as nx +from networkx.utils import not_implemented_for + +__all__ = [ + "eccentricity", + "diameter", + "harmonic_diameter", + "radius", + "periphery", + "center", + "barycenter", + "resistance_distance", + "kemeny_constant", + "effective_graph_resistance", +] + + +def _extrema_bounding(G, compute="diameter", weight=None): + """Compute requested extreme distance metric of undirected graph G + + Computation is based on smart lower and upper bounds, and in practice + linear in the number of nodes, rather than quadratic (except for some + border cases such as complete graphs or circle shaped graphs). + + Parameters + ---------- + G : NetworkX graph + An undirected graph + + compute : string denoting the requesting metric + "diameter" for the maximal eccentricity value, + "radius" for the minimal eccentricity value, + "periphery" for the set of nodes with eccentricity equal to the diameter, + "center" for the set of nodes with eccentricity equal to the radius, + "eccentricities" for the maximum distance from each node to all other nodes in G + + weight : string, function, or None + If this is a string, then edge weights will be accessed via the + edge attribute with this key (that is, the weight of the edge + joining `u` to `v` will be ``G.edges[u, v][weight]``). If no + such edge attribute exists, the weight of the edge is assumed to + be one. + + If this is a function, the weight of an edge is the value + returned by the function. The function must accept exactly three + positional arguments: the two endpoints of an edge and the + dictionary of edge attributes for that edge. The function must + return a number. + + If this is None, every edge has weight/distance/cost 1. + + Weights stored as floating point values can lead to small round-off + errors in distances. Use integer weights to avoid this. + + Weights should be positive, since they are distances. + + Returns + ------- + value : value of the requested metric + int for "diameter" and "radius" or + list of nodes for "center" and "periphery" or + dictionary of eccentricity values keyed by node for "eccentricities" + + Raises + ------ + NetworkXError + If the graph consists of multiple components + ValueError + If `compute` is not one of "diameter", "radius", "periphery", "center", or "eccentricities". + + Notes + ----- + This algorithm was proposed in [1]_ and discussed further in [2]_ and [3]_. + + References + ---------- + .. [1] F. W. Takes, W. A. Kosters, + "Determining the diameter of small world networks." + Proceedings of the 20th ACM international conference on Information and knowledge management, 2011 + https://dl.acm.org/doi/abs/10.1145/2063576.2063748 + .. [2] F. W. Takes, W. A. Kosters, + "Computing the Eccentricity Distribution of Large Graphs." + Algorithms, 2013 + https://www.mdpi.com/1999-4893/6/1/100 + .. [3] M. Borassi, P. Crescenzi, M. Habib, W. A. Kosters, A. Marino, F. W. Takes, + "Fast diameter and radius BFS-based computation in (weakly connected) real-world graphs: With an application to the six degrees of separation games. " + Theoretical Computer Science, 2015 + https://www.sciencedirect.com/science/article/pii/S0304397515001644 + """ + # init variables + degrees = dict(G.degree()) # start with the highest degree node + minlowernode = max(degrees, key=degrees.get) + N = len(degrees) # number of nodes + # alternate between smallest lower and largest upper bound + high = False + # status variables + ecc_lower = dict.fromkeys(G, 0) + ecc_upper = dict.fromkeys(G, N) + candidates = set(G) + + # (re)set bound extremes + minlower = N + maxlower = 0 + minupper = N + maxupper = 0 + + # repeat the following until there are no more candidates + while candidates: + if high: + current = maxuppernode # select node with largest upper bound + else: + current = minlowernode # select node with smallest lower bound + high = not high + + # get distances from/to current node and derive eccentricity + dist = nx.shortest_path_length(G, source=current, weight=weight) + + if len(dist) != N: + msg = "Cannot compute metric because graph is not connected." + raise nx.NetworkXError(msg) + current_ecc = max(dist.values()) + + # print status update + # print ("ecc of " + str(current) + " (" + str(ecc_lower[current]) + "/" + # + str(ecc_upper[current]) + ", deg: " + str(dist[current]) + ") is " + # + str(current_ecc)) + # print(ecc_upper) + + # (re)set bound extremes + maxuppernode = None + minlowernode = None + + # update node bounds + for i in candidates: + # update eccentricity bounds + d = dist[i] + ecc_lower[i] = low = max(ecc_lower[i], max(d, (current_ecc - d))) + ecc_upper[i] = upp = min(ecc_upper[i], current_ecc + d) + + # update min/max values of lower and upper bounds + minlower = min(ecc_lower[i], minlower) + maxlower = max(ecc_lower[i], maxlower) + minupper = min(ecc_upper[i], minupper) + maxupper = max(ecc_upper[i], maxupper) + + # update candidate set + if compute == "diameter": + ruled_out = { + i + for i in candidates + if ecc_upper[i] <= maxlower and 2 * ecc_lower[i] >= maxupper + } + elif compute == "radius": + ruled_out = { + i + for i in candidates + if ecc_lower[i] >= minupper and ecc_upper[i] + 1 <= 2 * minlower + } + elif compute == "periphery": + ruled_out = { + i + for i in candidates + if ecc_upper[i] < maxlower + and (maxlower == maxupper or ecc_lower[i] > maxupper) + } + elif compute == "center": + ruled_out = { + i + for i in candidates + if ecc_lower[i] > minupper + and (minlower == minupper or ecc_upper[i] + 1 < 2 * minlower) + } + elif compute == "eccentricities": + ruled_out = set() + else: + msg = "compute must be one of 'diameter', 'radius', 'periphery', 'center', 'eccentricities'" + raise ValueError(msg) + + ruled_out.update(i for i in candidates if ecc_lower[i] == ecc_upper[i]) + candidates -= ruled_out + + # for i in ruled_out: + # print("removing %g: ecc_u: %g maxl: %g ecc_l: %g maxu: %g"% + # (i,ecc_upper[i],maxlower,ecc_lower[i],maxupper)) + # print("node %g: ecc_u: %g maxl: %g ecc_l: %g maxu: %g"% + # (4,ecc_upper[4],maxlower,ecc_lower[4],maxupper)) + # print("NODE 4: %g"%(ecc_upper[4] <= maxlower)) + # print("NODE 4: %g"%(2 * ecc_lower[4] >= maxupper)) + # print("NODE 4: %g"%(ecc_upper[4] <= maxlower + # and 2 * ecc_lower[4] >= maxupper)) + + # updating maxuppernode and minlowernode for selection in next round + for i in candidates: + if ( + minlowernode is None + or ( + ecc_lower[i] == ecc_lower[minlowernode] + and degrees[i] > degrees[minlowernode] + ) + or (ecc_lower[i] < ecc_lower[minlowernode]) + ): + minlowernode = i + + if ( + maxuppernode is None + or ( + ecc_upper[i] == ecc_upper[maxuppernode] + and degrees[i] > degrees[maxuppernode] + ) + or (ecc_upper[i] > ecc_upper[maxuppernode]) + ): + maxuppernode = i + + # print status update + # print (" min=" + str(minlower) + "/" + str(minupper) + + # " max=" + str(maxlower) + "/" + str(maxupper) + + # " candidates: " + str(len(candidates))) + # print("cand:",candidates) + # print("ecc_l",ecc_lower) + # print("ecc_u",ecc_upper) + # wait = input("press Enter to continue") + + # return the correct value of the requested metric + if compute == "diameter": + return maxlower + if compute == "radius": + return minupper + if compute == "periphery": + p = [v for v in G if ecc_lower[v] == maxlower] + return p + if compute == "center": + c = [v for v in G if ecc_upper[v] == minupper] + return c + if compute == "eccentricities": + return ecc_lower + return None + + +@nx._dispatchable(edge_attrs="weight") +def eccentricity(G, v=None, sp=None, weight=None): + """Returns the eccentricity of nodes in G. + + The eccentricity of a node v is the maximum distance from v to + all other nodes in G. + + Parameters + ---------- + G : NetworkX graph + A graph + + v : node, optional + Return value of specified node + + sp : dict of dicts, optional + All pairs shortest path lengths as a dictionary of dictionaries + + weight : string, function, or None (default=None) + If this is a string, then edge weights will be accessed via the + edge attribute with this key (that is, the weight of the edge + joining `u` to `v` will be ``G.edges[u, v][weight]``). If no + such edge attribute exists, the weight of the edge is assumed to + be one. + + If this is a function, the weight of an edge is the value + returned by the function. The function must accept exactly three + positional arguments: the two endpoints of an edge and the + dictionary of edge attributes for that edge. The function must + return a number. + + If this is None, every edge has weight/distance/cost 1. + + Weights stored as floating point values can lead to small round-off + errors in distances. Use integer weights to avoid this. + + Weights should be positive, since they are distances. + + Returns + ------- + ecc : dictionary + A dictionary of eccentricity values keyed by node. + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> dict(nx.eccentricity(G)) + {1: 2, 2: 3, 3: 2, 4: 2, 5: 3} + + >>> dict( + ... nx.eccentricity(G, v=[1, 5]) + ... ) # This returns the eccentricity of node 1 & 5 + {1: 2, 5: 3} + + """ + # if v is None: # none, use entire graph + # nodes=G.nodes() + # elif v in G: # is v a single node + # nodes=[v] + # else: # assume v is a container of nodes + # nodes=v + order = G.order() + e = {} + for n in G.nbunch_iter(v): + if sp is None: + length = nx.shortest_path_length(G, source=n, weight=weight) + + L = len(length) + else: + try: + length = sp[n] + L = len(length) + except TypeError as err: + raise nx.NetworkXError('Format of "sp" is invalid.') from err + if L != order: + if G.is_directed(): + msg = ( + "Found infinite path length because the digraph is not" + " strongly connected" + ) + else: + msg = "Found infinite path length because the graph is not" " connected" + raise nx.NetworkXError(msg) + + e[n] = max(length.values()) + + if v in G: + return e[v] # return single value + return e + + +@nx._dispatchable(edge_attrs="weight") +def diameter(G, e=None, usebounds=False, weight=None): + """Returns the diameter of the graph G. + + The diameter is the maximum eccentricity. + + Parameters + ---------- + G : NetworkX graph + A graph + + e : eccentricity dictionary, optional + A precomputed dictionary of eccentricities. + + weight : string, function, or None + If this is a string, then edge weights will be accessed via the + edge attribute with this key (that is, the weight of the edge + joining `u` to `v` will be ``G.edges[u, v][weight]``). If no + such edge attribute exists, the weight of the edge is assumed to + be one. + + If this is a function, the weight of an edge is the value + returned by the function. The function must accept exactly three + positional arguments: the two endpoints of an edge and the + dictionary of edge attributes for that edge. The function must + return a number. + + If this is None, every edge has weight/distance/cost 1. + + Weights stored as floating point values can lead to small round-off + errors in distances. Use integer weights to avoid this. + + Weights should be positive, since they are distances. + + Returns + ------- + d : integer + Diameter of graph + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> nx.diameter(G) + 3 + + See Also + -------- + eccentricity + """ + if usebounds is True and e is None and not G.is_directed(): + return _extrema_bounding(G, compute="diameter", weight=weight) + if e is None: + e = eccentricity(G, weight=weight) + return max(e.values()) + + +@nx._dispatchable +def harmonic_diameter(G, sp=None): + """Returns the harmonic diameter of the graph G. + + The harmonic diameter of a graph is the harmonic mean of the distances + between all pairs of distinct vertices. Graphs that are not strongly + connected have infinite diameter and mean distance, making such + measures not useful. Restricting the diameter or mean distance to + finite distances yields paradoxical values (e.g., a perfect match + would have diameter one). The harmonic mean handles gracefully + infinite distances (e.g., a perfect match has harmonic diameter equal + to the number of vertices minus one), making it possible to assign a + meaningful value to all graphs. + + Note that in [1] the harmonic diameter is called "connectivity length": + however, "harmonic diameter" is a more standard name from the + theory of metric spaces. The name "harmonic mean distance" is perhaps + a more descriptive name, but is not used in the literature, so we use the + name "harmonic diameter" here. + + Parameters + ---------- + G : NetworkX graph + A graph + + sp : dict of dicts, optional + All-pairs shortest path lengths as a dictionary of dictionaries + + Returns + ------- + hd : float + Harmonic diameter of graph + + References + ---------- + .. [1] Massimo Marchiori and Vito Latora, "Harmony in the small-world". + *Physica A: Statistical Mechanics and Its Applications* + 285(3-4), pages 539-546, 2000. + <https://doi.org/10.1016/S0378-4371(00)00311-3> + """ + order = G.order() + + sum_invd = 0 + for n in G: + if sp is None: + length = nx.single_source_shortest_path_length(G, n) + else: + try: + length = sp[n] + L = len(length) + except TypeError as err: + raise nx.NetworkXError('Format of "sp" is invalid.') from err + + for d in length.values(): + # Note that this will skip the zero distance from n to itself, + # as it should be, but also zero-weight paths in weighted graphs. + if d != 0: + sum_invd += 1 / d + + if sum_invd != 0: + return order * (order - 1) / sum_invd + if order > 1: + return math.inf + return math.nan + + +@nx._dispatchable(edge_attrs="weight") +def periphery(G, e=None, usebounds=False, weight=None): + """Returns the periphery of the graph G. + + The periphery is the set of nodes with eccentricity equal to the diameter. + + Parameters + ---------- + G : NetworkX graph + A graph + + e : eccentricity dictionary, optional + A precomputed dictionary of eccentricities. + + weight : string, function, or None + If this is a string, then edge weights will be accessed via the + edge attribute with this key (that is, the weight of the edge + joining `u` to `v` will be ``G.edges[u, v][weight]``). If no + such edge attribute exists, the weight of the edge is assumed to + be one. + + If this is a function, the weight of an edge is the value + returned by the function. The function must accept exactly three + positional arguments: the two endpoints of an edge and the + dictionary of edge attributes for that edge. The function must + return a number. + + If this is None, every edge has weight/distance/cost 1. + + Weights stored as floating point values can lead to small round-off + errors in distances. Use integer weights to avoid this. + + Weights should be positive, since they are distances. + + Returns + ------- + p : list + List of nodes in periphery + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> nx.periphery(G) + [2, 5] + + See Also + -------- + barycenter + center + """ + if usebounds is True and e is None and not G.is_directed(): + return _extrema_bounding(G, compute="periphery", weight=weight) + if e is None: + e = eccentricity(G, weight=weight) + diameter = max(e.values()) + p = [v for v in e if e[v] == diameter] + return p + + +@nx._dispatchable(edge_attrs="weight") +def radius(G, e=None, usebounds=False, weight=None): + """Returns the radius of the graph G. + + The radius is the minimum eccentricity. + + Parameters + ---------- + G : NetworkX graph + A graph + + e : eccentricity dictionary, optional + A precomputed dictionary of eccentricities. + + weight : string, function, or None + If this is a string, then edge weights will be accessed via the + edge attribute with this key (that is, the weight of the edge + joining `u` to `v` will be ``G.edges[u, v][weight]``). If no + such edge attribute exists, the weight of the edge is assumed to + be one. + + If this is a function, the weight of an edge is the value + returned by the function. The function must accept exactly three + positional arguments: the two endpoints of an edge and the + dictionary of edge attributes for that edge. The function must + return a number. + + If this is None, every edge has weight/distance/cost 1. + + Weights stored as floating point values can lead to small round-off + errors in distances. Use integer weights to avoid this. + + Weights should be positive, since they are distances. + + Returns + ------- + r : integer + Radius of graph + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> nx.radius(G) + 2 + + """ + if usebounds is True and e is None and not G.is_directed(): + return _extrema_bounding(G, compute="radius", weight=weight) + if e is None: + e = eccentricity(G, weight=weight) + return min(e.values()) + + +@nx._dispatchable(edge_attrs="weight") +def center(G, e=None, usebounds=False, weight=None): + """Returns the center of the graph G. + + The center is the set of nodes with eccentricity equal to radius. + + Parameters + ---------- + G : NetworkX graph + A graph + + e : eccentricity dictionary, optional + A precomputed dictionary of eccentricities. + + weight : string, function, or None + If this is a string, then edge weights will be accessed via the + edge attribute with this key (that is, the weight of the edge + joining `u` to `v` will be ``G.edges[u, v][weight]``). If no + such edge attribute exists, the weight of the edge is assumed to + be one. + + If this is a function, the weight of an edge is the value + returned by the function. The function must accept exactly three + positional arguments: the two endpoints of an edge and the + dictionary of edge attributes for that edge. The function must + return a number. + + If this is None, every edge has weight/distance/cost 1. + + Weights stored as floating point values can lead to small round-off + errors in distances. Use integer weights to avoid this. + + Weights should be positive, since they are distances. + + Returns + ------- + c : list + List of nodes in center + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> list(nx.center(G)) + [1, 3, 4] + + See Also + -------- + barycenter + periphery + """ + if usebounds is True and e is None and not G.is_directed(): + return _extrema_bounding(G, compute="center", weight=weight) + if e is None: + e = eccentricity(G, weight=weight) + radius = min(e.values()) + p = [v for v in e if e[v] == radius] + return p + + +@nx._dispatchable(edge_attrs="weight", mutates_input={"attr": 2}) +def barycenter(G, weight=None, attr=None, sp=None): + r"""Calculate barycenter of a connected graph, optionally with edge weights. + + The :dfn:`barycenter` a + :func:`connected <networkx.algorithms.components.is_connected>` graph + :math:`G` is the subgraph induced by the set of its nodes :math:`v` + minimizing the objective function + + .. math:: + + \sum_{u \in V(G)} d_G(u, v), + + where :math:`d_G` is the (possibly weighted) :func:`path length + <networkx.algorithms.shortest_paths.generic.shortest_path_length>`. + The barycenter is also called the :dfn:`median`. See [West01]_, p. 78. + + Parameters + ---------- + G : :class:`networkx.Graph` + The connected graph :math:`G`. + weight : :class:`str`, optional + Passed through to + :func:`~networkx.algorithms.shortest_paths.generic.shortest_path_length`. + attr : :class:`str`, optional + If given, write the value of the objective function to each node's + `attr` attribute. Otherwise do not store the value. + sp : dict of dicts, optional + All pairs shortest path lengths as a dictionary of dictionaries + + Returns + ------- + list + Nodes of `G` that induce the barycenter of `G`. + + Raises + ------ + NetworkXNoPath + If `G` is disconnected. `G` may appear disconnected to + :func:`barycenter` if `sp` is given but is missing shortest path + lengths for any pairs. + ValueError + If `sp` and `weight` are both given. + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> nx.barycenter(G) + [1, 3, 4] + + See Also + -------- + center + periphery + """ + if sp is None: + sp = nx.shortest_path_length(G, weight=weight) + else: + sp = sp.items() + if weight is not None: + raise ValueError("Cannot use both sp, weight arguments together") + smallest, barycenter_vertices, n = float("inf"), [], len(G) + for v, dists in sp: + if len(dists) < n: + raise nx.NetworkXNoPath( + f"Input graph {G} is disconnected, so every induced subgraph " + "has infinite barycentricity." + ) + barycentricity = sum(dists.values()) + if attr is not None: + G.nodes[v][attr] = barycentricity + if barycentricity < smallest: + smallest = barycentricity + barycenter_vertices = [v] + elif barycentricity == smallest: + barycenter_vertices.append(v) + if attr is not None: + nx._clear_cache(G) + return barycenter_vertices + + +@not_implemented_for("directed") +@nx._dispatchable(edge_attrs="weight") +def resistance_distance(G, nodeA=None, nodeB=None, weight=None, invert_weight=True): + """Returns the resistance distance between pairs of nodes in graph G. + + The resistance distance between two nodes of a graph is akin to treating + the graph as a grid of resistors with a resistance equal to the provided + weight [1]_, [2]_. + + If weight is not provided, then a weight of 1 is used for all edges. + + If two nodes are the same, the resistance distance is zero. + + Parameters + ---------- + G : NetworkX graph + A graph + + nodeA : node or None, optional (default=None) + A node within graph G. + If None, compute resistance distance using all nodes as source nodes. + + nodeB : node or None, optional (default=None) + A node within graph G. + If None, compute resistance distance using all nodes as target nodes. + + weight : string or None, optional (default=None) + The edge data key used to compute the resistance distance. + If None, then each edge has weight 1. + + invert_weight : boolean (default=True) + Proper calculation of resistance distance requires building the + Laplacian matrix with the reciprocal of the weight. Not required + if the weight is already inverted. Weight cannot be zero. + + Returns + ------- + rd : dict or float + If `nodeA` and `nodeB` are given, resistance distance between `nodeA` + and `nodeB`. If `nodeA` or `nodeB` is unspecified (the default), a + dictionary of nodes with resistance distances as the value. + + Raises + ------ + NetworkXNotImplemented + If `G` is a directed graph. + + NetworkXError + If `G` is not connected, or contains no nodes, + or `nodeA` is not in `G` or `nodeB` is not in `G`. + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> round(nx.resistance_distance(G, 1, 3), 10) + 0.625 + + Notes + ----- + The implementation is based on Theorem A in [2]_. Self-loops are ignored. + Multi-edges are contracted in one edge with weight equal to the harmonic sum of the weights. + + References + ---------- + .. [1] Wikipedia + "Resistance distance." + https://en.wikipedia.org/wiki/Resistance_distance + .. [2] D. J. Klein and M. Randic. + Resistance distance. + J. of Math. Chem. 12:81-95, 1993. + """ + import numpy as np + + if len(G) == 0: + raise nx.NetworkXError("Graph G must contain at least one node.") + if not nx.is_connected(G): + raise nx.NetworkXError("Graph G must be strongly connected.") + if nodeA is not None and nodeA not in G: + raise nx.NetworkXError("Node A is not in graph G.") + if nodeB is not None and nodeB not in G: + raise nx.NetworkXError("Node B is not in graph G.") + + G = G.copy() + node_list = list(G) + + # Invert weights + if invert_weight and weight is not None: + if G.is_multigraph(): + for u, v, k, d in G.edges(keys=True, data=True): + d[weight] = 1 / d[weight] + else: + for u, v, d in G.edges(data=True): + d[weight] = 1 / d[weight] + + # Compute resistance distance using the Pseudo-inverse of the Laplacian + # Self-loops are ignored + L = nx.laplacian_matrix(G, weight=weight).todense() + Linv = np.linalg.pinv(L, hermitian=True) + + # Return relevant distances + if nodeA is not None and nodeB is not None: + i = node_list.index(nodeA) + j = node_list.index(nodeB) + return Linv.item(i, i) + Linv.item(j, j) - Linv.item(i, j) - Linv.item(j, i) + + elif nodeA is not None: + i = node_list.index(nodeA) + d = {} + for n in G: + j = node_list.index(n) + d[n] = Linv.item(i, i) + Linv.item(j, j) - Linv.item(i, j) - Linv.item(j, i) + return d + + elif nodeB is not None: + j = node_list.index(nodeB) + d = {} + for n in G: + i = node_list.index(n) + d[n] = Linv.item(i, i) + Linv.item(j, j) - Linv.item(i, j) - Linv.item(j, i) + return d + + else: + d = {} + for n in G: + i = node_list.index(n) + d[n] = {} + for n2 in G: + j = node_list.index(n2) + d[n][n2] = ( + Linv.item(i, i) + + Linv.item(j, j) + - Linv.item(i, j) + - Linv.item(j, i) + ) + return d + + +@not_implemented_for("directed") +@nx._dispatchable(edge_attrs="weight") +def effective_graph_resistance(G, weight=None, invert_weight=True): + """Returns the Effective graph resistance of G. + + Also known as the Kirchhoff index. + + The effective graph resistance is defined as the sum + of the resistance distance of every node pair in G [1]_. + + If weight is not provided, then a weight of 1 is used for all edges. + + The effective graph resistance of a disconnected graph is infinite. + + Parameters + ---------- + G : NetworkX graph + A graph + + weight : string or None, optional (default=None) + The edge data key used to compute the effective graph resistance. + If None, then each edge has weight 1. + + invert_weight : boolean (default=True) + Proper calculation of resistance distance requires building the + Laplacian matrix with the reciprocal of the weight. Not required + if the weight is already inverted. Weight cannot be zero. + + Returns + ------- + RG : float + The effective graph resistance of `G`. + + Raises + ------ + NetworkXNotImplemented + If `G` is a directed graph. + + NetworkXError + If `G` does not contain any nodes. + + Examples + -------- + >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)]) + >>> round(nx.effective_graph_resistance(G), 10) + 10.25 + + Notes + ----- + The implementation is based on Theorem 2.2 in [2]_. Self-loops are ignored. + Multi-edges are contracted in one edge with weight equal to the harmonic sum of the weights. + + References + ---------- + .. [1] Wolfram + "Kirchhoff Index." + https://mathworld.wolfram.com/KirchhoffIndex.html + .. [2] W. Ellens, F. M. Spieksma, P. Van Mieghem, A. Jamakovic, R. E. Kooij. + Effective graph resistance. + Lin. Alg. Appl. 435:2491-2506, 2011. + """ + import numpy as np + + if len(G) == 0: + raise nx.NetworkXError("Graph G must contain at least one node.") + + # Disconnected graphs have infinite Effective graph resistance + if not nx.is_connected(G): + return float("inf") + + # Invert weights + G = G.copy() + if invert_weight and weight is not None: + if G.is_multigraph(): + for u, v, k, d in G.edges(keys=True, data=True): + d[weight] = 1 / d[weight] + else: + for u, v, d in G.edges(data=True): + d[weight] = 1 / d[weight] + + # Get Laplacian eigenvalues + mu = np.sort(nx.laplacian_spectrum(G, weight=weight)) + + # Compute Effective graph resistance based on spectrum of the Laplacian + # Self-loops are ignored + return float(np.sum(1 / mu[1:]) * G.number_of_nodes()) + + +@nx.utils.not_implemented_for("directed") +@nx._dispatchable(edge_attrs="weight") +def kemeny_constant(G, *, weight=None): + """Returns the Kemeny constant of the given graph. + + The *Kemeny constant* (or Kemeny's constant) of a graph `G` + can be computed by regarding the graph as a Markov chain. + The Kemeny constant is then the expected number of time steps + to transition from a starting state i to a random destination state + sampled from the Markov chain's stationary distribution. + The Kemeny constant is independent of the chosen initial state [1]_. + + The Kemeny constant measures the time needed for spreading + across a graph. Low values indicate a closely connected graph + whereas high values indicate a spread-out graph. + + If weight is not provided, then a weight of 1 is used for all edges. + + Since `G` represents a Markov chain, the weights must be positive. + + Parameters + ---------- + G : NetworkX graph + + weight : string or None, optional (default=None) + The edge data key used to compute the Kemeny constant. + If None, then each edge has weight 1. + + Returns + ------- + float + The Kemeny constant of the graph `G`. + + Raises + ------ + NetworkXNotImplemented + If the graph `G` is directed. + + NetworkXError + If the graph `G` is not connected, or contains no nodes, + or has edges with negative weights. + + Examples + -------- + >>> G = nx.complete_graph(5) + >>> round(nx.kemeny_constant(G), 10) + 3.2 + + Notes + ----- + The implementation is based on equation (3.3) in [2]_. + Self-loops are allowed and indicate a Markov chain where + the state can remain the same. Multi-edges are contracted + in one edge with weight equal to the sum of the weights. + + References + ---------- + .. [1] Wikipedia + "Kemeny's constant." + https://en.wikipedia.org/wiki/Kemeny%27s_constant + .. [2] Lovász L. + Random walks on graphs: A survey. + Paul Erdös is Eighty, vol. 2, Bolyai Society, + Mathematical Studies, Keszthely, Hungary (1993), pp. 1-46 + """ + import numpy as np + import scipy as sp + + if len(G) == 0: + raise nx.NetworkXError("Graph G must contain at least one node.") + if not nx.is_connected(G): + raise nx.NetworkXError("Graph G must be connected.") + if nx.is_negatively_weighted(G, weight=weight): + raise nx.NetworkXError("The weights of graph G must be nonnegative.") + + # Compute matrix H = D^-1/2 A D^-1/2 + A = nx.adjacency_matrix(G, weight=weight) + n, m = A.shape + diags = A.sum(axis=1) + with np.errstate(divide="ignore"): + diags_sqrt = 1.0 / np.sqrt(diags) + diags_sqrt[np.isinf(diags_sqrt)] = 0 + DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, m, n, format="csr")) + H = DH @ (A @ DH) + + # Compute eigenvalues of H + eig = np.sort(sp.linalg.eigvalsh(H.todense())) + + # Compute the Kemeny constant + return float(np.sum(1 / (1 - eig[:-1]))) |