## Mercurial > repos > shellac > sam_consensus_v3

### view env/lib/python3.9/site-packages/networkx/algorithms/smallworld.py @ 0:4f3585e2f14b draft default tip

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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |

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"""Functions for estimating the small-world-ness of graphs. A small world network is characterized by a small average shortest path length, and a large clustering coefficient. Small-worldness is commonly measured with the coefficient sigma or omega. Both coefficients compare the average clustering coefficient and shortest path length of a given graph against the same quantities for an equivalent random or lattice graph. For more information, see the Wikipedia article on small-world network [1]_. .. [1] Small-world network:: https://en.wikipedia.org/wiki/Small-world_network """ import networkx as nx from networkx.utils import not_implemented_for from networkx.utils import py_random_state __all__ = ["random_reference", "lattice_reference", "sigma", "omega"] @py_random_state(3) @not_implemented_for("directed") @not_implemented_for("multigraph") def random_reference(G, niter=1, connectivity=True, seed=None): """Compute a random graph by swapping edges of a given graph. Parameters ---------- G : graph An undirected graph with 4 or more nodes. niter : integer (optional, default=1) An edge is rewired approximately `niter` times. connectivity : boolean (optional, default=True) When True, ensure connectivity for the randomized graph. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- G : graph The randomized graph. Notes ----- The implementation is adapted from the algorithm by Maslov and Sneppen (2002) [1]_. References ---------- .. [1] Maslov, Sergei, and Kim Sneppen. "Specificity and stability in topology of protein networks." Science 296.5569 (2002): 910-913. """ if G.is_directed(): msg = "random_reference() not defined for directed graphs." raise nx.NetworkXError(msg) if len(G) < 4: raise nx.NetworkXError("Graph has less than four nodes.") from networkx.utils import cumulative_distribution, discrete_sequence local_conn = nx.connectivity.local_edge_connectivity G = G.copy() keys, degrees = zip(*G.degree()) # keys, degree cdf = cumulative_distribution(degrees) # cdf of degree nnodes = len(G) nedges = nx.number_of_edges(G) niter = niter * nedges ntries = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2)) swapcount = 0 for i in range(niter): n = 0 while n < ntries: # pick two random edges without creating edge list # choose source node indices from discrete distribution (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed) if ai == ci: continue # same source, skip a = keys[ai] # convert index to label c = keys[ci] # choose target uniformly from neighbors b = seed.choice(list(G.neighbors(a))) d = seed.choice(list(G.neighbors(c))) bi = keys.index(b) di = keys.index(d) if b in [a, c, d] or d in [a, b, c]: continue # all vertices should be different # don't create parallel edges if (d not in G[a]) and (b not in G[c]): G.add_edge(a, d) G.add_edge(c, b) G.remove_edge(a, b) G.remove_edge(c, d) # Check if the graph is still connected if connectivity and local_conn(G, a, b) == 0: # Not connected, revert the swap G.remove_edge(a, d) G.remove_edge(c, b) G.add_edge(a, b) G.add_edge(c, d) else: swapcount += 1 break n += 1 return G @py_random_state(4) @not_implemented_for("directed") @not_implemented_for("multigraph") def lattice_reference(G, niter=1, D=None, connectivity=True, seed=None): """Latticize the given graph by swapping edges. Parameters ---------- G : graph An undirected graph with 4 or more nodes. niter : integer (optional, default=1) An edge is rewired approximatively niter times. D : numpy.array (optional, default=None) Distance to the diagonal matrix. connectivity : boolean (optional, default=True) Ensure connectivity for the latticized graph when set to True. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- G : graph The latticized graph. Notes ----- The implementation is adapted from the algorithm by Sporns et al. [1]_. which is inspired from the original work by Maslov and Sneppen(2002) [2]_. References ---------- .. [1] Sporns, Olaf, and Jonathan D. Zwi. "The small world of the cerebral cortex." Neuroinformatics 2.2 (2004): 145-162. .. [2] Maslov, Sergei, and Kim Sneppen. "Specificity and stability in topology of protein networks." Science 296.5569 (2002): 910-913. """ import numpy as np from networkx.utils import cumulative_distribution, discrete_sequence local_conn = nx.connectivity.local_edge_connectivity if G.is_directed(): msg = "lattice_reference() not defined for directed graphs." raise nx.NetworkXError(msg) if len(G) < 4: raise nx.NetworkXError("Graph has less than four nodes.") # Instead of choosing uniformly at random from a generated edge list, # this algorithm chooses nonuniformly from the set of nodes with # probability weighted by degree. G = G.copy() keys, degrees = zip(*G.degree()) # keys, degree cdf = cumulative_distribution(degrees) # cdf of degree nnodes = len(G) nedges = nx.number_of_edges(G) if D is None: D = np.zeros((nnodes, nnodes)) un = np.arange(1, nnodes) um = np.arange(nnodes - 1, 0, -1) u = np.append((0,), np.where(un < um, un, um)) for v in range(int(np.ceil(nnodes / 2))): D[nnodes - v - 1, :] = np.append(u[v + 1 :], u[: v + 1]) D[v, :] = D[nnodes - v - 1, :][::-1] niter = niter * nedges ntries = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2)) swapcount = 0 for i in range(niter): n = 0 while n < ntries: # pick two random edges without creating edge list # choose source node indices from discrete distribution (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed) if ai == ci: continue # same source, skip a = keys[ai] # convert index to label c = keys[ci] # choose target uniformly from neighbors b = seed.choice(list(G.neighbors(a))) d = seed.choice(list(G.neighbors(c))) bi = keys.index(b) di = keys.index(d) if b in [a, c, d] or d in [a, b, c]: continue # all vertices should be different # don't create parallel edges if (d not in G[a]) and (b not in G[c]): if D[ai, bi] + D[ci, di] >= D[ai, ci] + D[bi, di]: # only swap if we get closer to the diagonal G.add_edge(a, d) G.add_edge(c, b) G.remove_edge(a, b) G.remove_edge(c, d) # Check if the graph is still connected if connectivity and local_conn(G, a, b) == 0: # Not connected, revert the swap G.remove_edge(a, d) G.remove_edge(c, b) G.add_edge(a, b) G.add_edge(c, d) else: swapcount += 1 break n += 1 return G @py_random_state(3) @not_implemented_for("directed") @not_implemented_for("multigraph") def sigma(G, niter=100, nrand=10, seed=None): """Returns the small-world coefficient (sigma) of the given graph. The small-world coefficient is defined as: sigma = C/Cr / L/Lr where C and L are respectively the average clustering coefficient and average shortest path length of G. Cr and Lr are respectively the average clustering coefficient and average shortest path length of an equivalent random graph. A graph is commonly classified as small-world if sigma>1. Parameters ---------- G : NetworkX graph An undirected graph. niter : integer (optional, default=100) Approximate number of rewiring per edge to compute the equivalent random graph. nrand : integer (optional, default=10) Number of random graphs generated to compute the average clustering coefficient (Cr) and average shortest path length (Lr). seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- sigma : float The small-world coefficient of G. Notes ----- The implementation is adapted from Humphries et al. [1]_ [2]_. References ---------- .. [1] The brainstem reticular formation is a small-world, not scale-free, network M. D. Humphries, K. Gurney and T. J. Prescott, Proc. Roy. Soc. B 2006 273, 503-511, doi:10.1098/rspb.2005.3354. .. [2] Humphries and Gurney (2008). "Network 'Small-World-Ness': A Quantitative Method for Determining Canonical Network Equivalence". PLoS One. 3 (4). PMID 18446219. doi:10.1371/journal.pone.0002051. """ import numpy as np # Compute the mean clustering coefficient and average shortest path length # for an equivalent random graph randMetrics = {"C": [], "L": []} for i in range(nrand): Gr = random_reference(G, niter=niter, seed=seed) randMetrics["C"].append(nx.transitivity(Gr)) randMetrics["L"].append(nx.average_shortest_path_length(Gr)) C = nx.transitivity(G) L = nx.average_shortest_path_length(G) Cr = np.mean(randMetrics["C"]) Lr = np.mean(randMetrics["L"]) sigma = (C / Cr) / (L / Lr) return sigma @py_random_state(3) @not_implemented_for("directed") @not_implemented_for("multigraph") def omega(G, niter=100, nrand=10, seed=None): """Returns the small-world coefficient (omega) of a graph The small-world coefficient of a graph G is: omega = Lr/L - C/Cl where C and L are respectively the average clustering coefficient and average shortest path length of G. Lr is the average shortest path length of an equivalent random graph and Cl is the average clustering coefficient of an equivalent lattice graph. The small-world coefficient (omega) ranges between -1 and 1. Values close to 0 means the G features small-world characteristics. Values close to -1 means G has a lattice shape whereas values close to 1 means G is a random graph. Parameters ---------- G : NetworkX graph An undirected graph. niter: integer (optional, default=100) Approximate number of rewiring per edge to compute the equivalent random graph. nrand: integer (optional, default=10) Number of random graphs generated to compute the average clustering coefficient (Cr) and average shortest path length (Lr). seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- omega : float The small-work coefficient (omega) Notes ----- The implementation is adapted from the algorithm by Telesford et al. [1]_. References ---------- .. [1] Telesford, Joyce, Hayasaka, Burdette, and Laurienti (2011). "The Ubiquity of Small-World Networks". Brain Connectivity. 1 (0038): 367-75. PMC 3604768. PMID 22432451. doi:10.1089/brain.2011.0038. """ import numpy as np # Compute the mean clustering coefficient and average shortest path length # for an equivalent random graph randMetrics = {"C": [], "L": []} for i in range(nrand): Gr = random_reference(G, niter=niter, seed=seed) Gl = lattice_reference(G, niter=niter, seed=seed) randMetrics["C"].append(nx.transitivity(Gl)) randMetrics["L"].append(nx.average_shortest_path_length(Gr)) C = nx.transitivity(G) L = nx.average_shortest_path_length(G) Cl = np.mean(randMetrics["C"]) Lr = np.mean(randMetrics["L"]) omega = (Lr / L) - (C / Cl) return omega