## Mercurial > repos > shellac > sam_consensus_v3

### view env/lib/python3.9/site-packages/networkx/algorithms/core.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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""" Find the k-cores of a graph. The k-core is found by recursively pruning nodes with degrees less than k. See the following references for details: An O(m) Algorithm for Cores Decomposition of Networks Vladimir Batagelj and Matjaz Zaversnik, 2003. https://arxiv.org/abs/cs.DS/0310049 Generalized Cores Vladimir Batagelj and Matjaz Zaversnik, 2002. https://arxiv.org/pdf/cs/0202039 For directed graphs a more general notion is that of D-cores which looks at (k, l) restrictions on (in, out) degree. The (k, k) D-core is the k-core. D-cores: Measuring Collaboration of Directed Graphs Based on Degeneracy Christos Giatsidis, Dimitrios M. Thilikos, Michalis Vazirgiannis, ICDM 2011. http://www.graphdegeneracy.org/dcores_ICDM_2011.pdf Multi-scale structure and topological anomaly detection via a new network \ statistic: The onion decomposition L. Hébert-Dufresne, J. A. Grochow, and A. Allard Scientific Reports 6, 31708 (2016) http://doi.org/10.1038/srep31708 """ import networkx as nx from networkx.exception import NetworkXError from networkx.utils import not_implemented_for __all__ = [ "core_number", "find_cores", "k_core", "k_shell", "k_crust", "k_corona", "k_truss", "onion_layers", ] @not_implemented_for("multigraph") def core_number(G): """Returns the core number for each vertex. A k-core is a maximal subgraph that contains nodes of degree k or more. The core number of a node is the largest value k of a k-core containing that node. Parameters ---------- G : NetworkX graph A graph or directed graph Returns ------- core_number : dictionary A dictionary keyed by node to the core number. Raises ------ NetworkXError The k-core is not implemented for graphs with self loops or parallel edges. Notes ----- Not implemented for graphs with parallel edges or self loops. For directed graphs the node degree is defined to be the in-degree + out-degree. References ---------- .. [1] An O(m) Algorithm for Cores Decomposition of Networks Vladimir Batagelj and Matjaz Zaversnik, 2003. https://arxiv.org/abs/cs.DS/0310049 """ if nx.number_of_selfloops(G) > 0: msg = ( "Input graph has self loops which is not permitted; " "Consider using G.remove_edges_from(nx.selfloop_edges(G))." ) raise NetworkXError(msg) degrees = dict(G.degree()) # Sort nodes by degree. nodes = sorted(degrees, key=degrees.get) bin_boundaries = [0] curr_degree = 0 for i, v in enumerate(nodes): if degrees[v] > curr_degree: bin_boundaries.extend([i] * (degrees[v] - curr_degree)) curr_degree = degrees[v] node_pos = {v: pos for pos, v in enumerate(nodes)} # The initial guess for the core number of a node is its degree. core = degrees nbrs = {v: list(nx.all_neighbors(G, v)) for v in G} for v in nodes: for u in nbrs[v]: if core[u] > core[v]: nbrs[u].remove(v) pos = node_pos[u] bin_start = bin_boundaries[core[u]] node_pos[u] = bin_start node_pos[nodes[bin_start]] = pos nodes[bin_start], nodes[pos] = nodes[pos], nodes[bin_start] bin_boundaries[core[u]] += 1 core[u] -= 1 return core find_cores = core_number def _core_subgraph(G, k_filter, k=None, core=None): """Returns the subgraph induced by nodes passing filter `k_filter`. Parameters ---------- G : NetworkX graph The graph or directed graph to process k_filter : filter function This function filters the nodes chosen. It takes three inputs: A node of G, the filter's cutoff, and the core dict of the graph. The function should return a Boolean value. k : int, optional The order of the core. If not specified use the max core number. This value is used as the cutoff for the filter. core : dict, optional Precomputed core numbers keyed by node for the graph `G`. If not specified, the core numbers will be computed from `G`. """ if core is None: core = core_number(G) if k is None: k = max(core.values()) nodes = (v for v in core if k_filter(v, k, core)) return G.subgraph(nodes).copy() def k_core(G, k=None, core_number=None): """Returns the k-core of G. A k-core is a maximal subgraph that contains nodes of degree k or more. Parameters ---------- G : NetworkX graph A graph or directed graph k : int, optional The order of the core. If not specified return the main core. core_number : dictionary, optional Precomputed core numbers for the graph G. Returns ------- G : NetworkX graph The k-core subgraph Raises ------ NetworkXError The k-core is not defined for graphs with self loops or parallel edges. Notes ----- The main core is the core with the largest degree. Not implemented for graphs with parallel edges or self loops. For directed graphs the node degree is defined to be the in-degree + out-degree. Graph, node, and edge attributes are copied to the subgraph. See Also -------- core_number References ---------- .. [1] An O(m) Algorithm for Cores Decomposition of Networks Vladimir Batagelj and Matjaz Zaversnik, 2003. https://arxiv.org/abs/cs.DS/0310049 """ def k_filter(v, k, c): return c[v] >= k return _core_subgraph(G, k_filter, k, core_number) def k_shell(G, k=None, core_number=None): """Returns the k-shell of G. The k-shell is the subgraph induced by nodes with core number k. That is, nodes in the k-core that are not in the (k+1)-core. Parameters ---------- G : NetworkX graph A graph or directed graph. k : int, optional The order of the shell. If not specified return the outer shell. core_number : dictionary, optional Precomputed core numbers for the graph G. Returns ------- G : NetworkX graph The k-shell subgraph Raises ------ NetworkXError The k-shell is not implemented for graphs with self loops or parallel edges. Notes ----- This is similar to k_corona but in that case only neighbors in the k-core are considered. Not implemented for graphs with parallel edges or self loops. For directed graphs the node degree is defined to be the in-degree + out-degree. Graph, node, and edge attributes are copied to the subgraph. See Also -------- core_number k_corona References ---------- .. [1] A model of Internet topology using k-shell decomposition Shai Carmi, Shlomo Havlin, Scott Kirkpatrick, Yuval Shavitt, and Eran Shir, PNAS July 3, 2007 vol. 104 no. 27 11150-11154 http://www.pnas.org/content/104/27/11150.full """ def k_filter(v, k, c): return c[v] == k return _core_subgraph(G, k_filter, k, core_number) def k_crust(G, k=None, core_number=None): """Returns the k-crust of G. The k-crust is the graph G with the k-core removed. Parameters ---------- G : NetworkX graph A graph or directed graph. k : int, optional The order of the shell. If not specified return the main crust. core_number : dictionary, optional Precomputed core numbers for the graph G. Returns ------- G : NetworkX graph The k-crust subgraph Raises ------ NetworkXError The k-crust is not implemented for graphs with self loops or parallel edges. Notes ----- This definition of k-crust is different than the definition in [1]_. The k-crust in [1]_ is equivalent to the k+1 crust of this algorithm. Not implemented for graphs with parallel edges or self loops. For directed graphs the node degree is defined to be the in-degree + out-degree. Graph, node, and edge attributes are copied to the subgraph. See Also -------- core_number References ---------- .. [1] A model of Internet topology using k-shell decomposition Shai Carmi, Shlomo Havlin, Scott Kirkpatrick, Yuval Shavitt, and Eran Shir, PNAS July 3, 2007 vol. 104 no. 27 11150-11154 http://www.pnas.org/content/104/27/11150.full """ # Default for k is one less than in _core_subgraph, so just inline. # Filter is c[v] <= k if core_number is None: core_number = find_cores(G) if k is None: k = max(core_number.values()) - 1 nodes = (v for v in core_number if core_number[v] <= k) return G.subgraph(nodes).copy() def k_corona(G, k, core_number=None): """Returns the k-corona of G. The k-corona is the subgraph of nodes in the k-core which have exactly k neighbours in the k-core. Parameters ---------- G : NetworkX graph A graph or directed graph k : int The order of the corona. core_number : dictionary, optional Precomputed core numbers for the graph G. Returns ------- G : NetworkX graph The k-corona subgraph Raises ------ NetworkXError The k-cornoa is not defined for graphs with self loops or parallel edges. Notes ----- Not implemented for graphs with parallel edges or self loops. For directed graphs the node degree is defined to be the in-degree + out-degree. Graph, node, and edge attributes are copied to the subgraph. See Also -------- core_number References ---------- .. [1] k -core (bootstrap) percolation on complex networks: Critical phenomena and nonlocal effects, A. V. Goltsev, S. N. Dorogovtsev, and J. F. F. Mendes, Phys. Rev. E 73, 056101 (2006) http://link.aps.org/doi/10.1103/PhysRevE.73.056101 """ def func(v, k, c): return c[v] == k and k == sum(1 for w in G[v] if c[w] >= k) return _core_subgraph(G, func, k, core_number) @not_implemented_for("directed") @not_implemented_for("multigraph") def k_truss(G, k): """Returns the k-truss of `G`. The k-truss is the maximal induced subgraph of `G` which contains at least three vertices where every edge is incident to at least `k-2` triangles. Parameters ---------- G : NetworkX graph An undirected graph k : int The order of the truss Returns ------- H : NetworkX graph The k-truss subgraph Raises ------ NetworkXError The k-truss is not defined for graphs with self loops or parallel edges or directed graphs. Notes ----- A k-clique is a (k-2)-truss and a k-truss is a (k+1)-core. Not implemented for digraphs or graphs with parallel edges or self loops. Graph, node, and edge attributes are copied to the subgraph. K-trusses were originally defined in [2] which states that the k-truss is the maximal induced subgraph where each edge belongs to at least `k-2` triangles. A more recent paper, [1], uses a slightly different definition requiring that each edge belong to at least `k` triangles. This implementation uses the original definition of `k-2` triangles. References ---------- .. [1] Bounds and Algorithms for k-truss. Paul Burkhardt, Vance Faber, David G. Harris, 2018. https://arxiv.org/abs/1806.05523v2 .. [2] Trusses: Cohesive Subgraphs for Social Network Analysis. Jonathan Cohen, 2005. """ H = G.copy() n_dropped = 1 while n_dropped > 0: n_dropped = 0 to_drop = [] seen = set() for u in H: nbrs_u = set(H[u]) seen.add(u) new_nbrs = [v for v in nbrs_u if v not in seen] for v in new_nbrs: if len(nbrs_u & set(H[v])) < (k - 2): to_drop.append((u, v)) H.remove_edges_from(to_drop) n_dropped = len(to_drop) H.remove_nodes_from(list(nx.isolates(H))) return H @not_implemented_for("multigraph") @not_implemented_for("directed") def onion_layers(G): """Returns the layer of each vertex in an onion decomposition of the graph. The onion decomposition refines the k-core decomposition by providing information on the internal organization of each k-shell. It is usually used alongside the `core numbers`. Parameters ---------- G : NetworkX graph A simple graph without self loops or parallel edges Returns ------- od_layers : dictionary A dictionary keyed by vertex to the onion layer. The layers are contiguous integers starting at 1. Raises ------ NetworkXError The onion decomposition is not implemented for graphs with self loops or parallel edges or for directed graphs. Notes ----- Not implemented for graphs with parallel edges or self loops. Not implemented for directed graphs. See Also -------- core_number References ---------- .. [1] Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition L. Hébert-Dufresne, J. A. Grochow, and A. Allard Scientific Reports 6, 31708 (2016) http://doi.org/10.1038/srep31708 .. [2] Percolation and the effective structure of complex networks A. Allard and L. Hébert-Dufresne Physical Review X 9, 011023 (2019) http://doi.org/10.1103/PhysRevX.9.011023 """ if nx.number_of_selfloops(G) > 0: msg = ( "Input graph contains self loops which is not permitted; " "Consider using G.remove_edges_from(nx.selfloop_edges(G))." ) raise NetworkXError(msg) # Dictionaries to register the k-core/onion decompositions. od_layers = {} # Adjacency list neighbors = {v: list(nx.all_neighbors(G, v)) for v in G} # Effective degree of nodes. degrees = dict(G.degree()) # Performs the onion decomposition. current_core = 1 current_layer = 1 # Sets vertices of degree 0 to layer 1, if any. isolated_nodes = [v for v in nx.isolates(G)] if len(isolated_nodes) > 0: for v in isolated_nodes: od_layers[v] = current_layer degrees.pop(v) current_layer = 2 # Finds the layer for the remaining nodes. while len(degrees) > 0: # Sets the order for looking at nodes. nodes = sorted(degrees, key=degrees.get) # Sets properly the current core. min_degree = degrees[nodes[0]] if min_degree > current_core: current_core = min_degree # Identifies vertices in the current layer. this_layer = [] for n in nodes: if degrees[n] > current_core: break this_layer.append(n) # Identifies the core/layer of the vertices in the current layer. for v in this_layer: od_layers[v] = current_layer for n in neighbors[v]: neighbors[n].remove(v) degrees[n] = degrees[n] - 1 degrees.pop(v) # Updates the layer count. current_layer = current_layer + 1 # Returns the dictionaries containing the onion layer of each vertices. return od_layers