164 lines
4.9 KiB
Python
164 lines
4.9 KiB
Python
"""Functions for generating graphs based on the "duplication" method.
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These graph generators start with a small initial graph then duplicate
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nodes and (partially) duplicate their edges. These functions are
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generally inspired by biological networks.
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"""
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import networkx as nx
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from networkx.exception import NetworkXError
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from networkx.utils import py_random_state
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__all__ = ["partial_duplication_graph", "duplication_divergence_graph"]
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@py_random_state(4)
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@nx._dispatch(graphs=None)
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def partial_duplication_graph(N, n, p, q, seed=None):
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"""Returns a random graph using the partial duplication model.
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Parameters
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----------
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N : int
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The total number of nodes in the final graph.
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n : int
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The number of nodes in the initial clique.
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p : float
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The probability of joining each neighbor of a node to the
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duplicate node. Must be a number in the between zero and one,
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inclusive.
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q : float
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The probability of joining the source node to the duplicate
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node. Must be a number in the between zero and one, inclusive.
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seed : integer, random_state, or None (default)
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Indicator of random number generation state.
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See :ref:`Randomness<randomness>`.
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Notes
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-----
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A graph of nodes is grown by creating a fully connected graph
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of size `n`. The following procedure is then repeated until
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a total of `N` nodes have been reached.
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1. A random node, *u*, is picked and a new node, *v*, is created.
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2. For each neighbor of *u* an edge from the neighbor to *v* is created
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with probability `p`.
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3. An edge from *u* to *v* is created with probability `q`.
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This algorithm appears in [1].
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This implementation allows the possibility of generating
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disconnected graphs.
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References
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----------
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.. [1] Knudsen Michael, and Carsten Wiuf. "A Markov chain approach to
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randomly grown graphs." Journal of Applied Mathematics 2008.
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<https://doi.org/10.1155/2008/190836>
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"""
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if p < 0 or p > 1 or q < 0 or q > 1:
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msg = "partial duplication graph must have 0 <= p, q <= 1."
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raise NetworkXError(msg)
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if n > N:
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raise NetworkXError("partial duplication graph must have n <= N.")
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G = nx.complete_graph(n)
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for new_node in range(n, N):
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# Pick a random vertex, u, already in the graph.
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src_node = seed.randint(0, new_node - 1)
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# Add a new vertex, v, to the graph.
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G.add_node(new_node)
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# For each neighbor of u...
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for neighbor_node in list(nx.all_neighbors(G, src_node)):
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# Add the neighbor to v with probability p.
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if seed.random() < p:
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G.add_edge(new_node, neighbor_node)
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# Join v and u with probability q.
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if seed.random() < q:
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G.add_edge(new_node, src_node)
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return G
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@py_random_state(2)
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@nx._dispatch(graphs=None)
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def duplication_divergence_graph(n, p, seed=None):
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"""Returns an undirected graph using the duplication-divergence model.
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A graph of `n` nodes is created by duplicating the initial nodes
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and retaining edges incident to the original nodes with a retention
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probability `p`.
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Parameters
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----------
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n : int
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The desired number of nodes in the graph.
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p : float
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The probability for retaining the edge of the replicated node.
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seed : integer, random_state, or None (default)
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Indicator of random number generation state.
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See :ref:`Randomness<randomness>`.
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Returns
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-------
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G : Graph
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Raises
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------
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NetworkXError
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If `p` is not a valid probability.
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If `n` is less than 2.
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Notes
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-----
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This algorithm appears in [1].
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This implementation disallows the possibility of generating
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disconnected graphs.
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References
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----------
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.. [1] I. Ispolatov, P. L. Krapivsky, A. Yuryev,
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"Duplication-divergence model of protein interaction network",
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Phys. Rev. E, 71, 061911, 2005.
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"""
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if p > 1 or p < 0:
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msg = f"NetworkXError p={p} is not in [0,1]."
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raise nx.NetworkXError(msg)
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if n < 2:
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msg = "n must be greater than or equal to 2"
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raise nx.NetworkXError(msg)
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G = nx.Graph()
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# Initialize the graph with two connected nodes.
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G.add_edge(0, 1)
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i = 2
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while i < n:
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# Choose a random node from current graph to duplicate.
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random_node = seed.choice(list(G))
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# Make the replica.
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G.add_node(i)
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# flag indicates whether at least one edge is connected on the replica.
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flag = False
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for nbr in G.neighbors(random_node):
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if seed.random() < p:
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# Link retention step.
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G.add_edge(i, nbr)
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flag = True
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if not flag:
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# Delete replica if no edges retained.
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G.remove_node(i)
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else:
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# Successful duplication.
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i += 1
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return G
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