Module xswap
Source code
from xswap import network_formats
from xswap import preprocessing
from xswap import prior
from xswap.permute import permute_edge_list
__version__ = '0.0.2'
__all__ = [
'network_formats.edges_to_matrix',
'network_formats.matrix_to_edges',
'permute_edge_list',
'preprocessing.load_str_edges',
'preprocessing.load_processed_edges',
'preprocessing.map_str_edges',
'prior.compute_xswap_occurrence_matrix',
'prior.compute_xswap_priors',
'prior.approximate_xswap_prior',
]
Sub-modules
xswap.network_formats
xswap.permute
xswap.preprocessing
xswap.prior
Functions
def permute_edge_list(edge_list, allow_self_loops=False, allow_antiparallel=False, multiplier=10, excluded_edges=set(), seed=0, max_malloc=4000000000)
-
Permute the edges of a graph using the XSwap method given by Hanhijärvi, et al. (doi.org/f3mn58). XSwap is a degree-preserving network randomization technique that selects edges, checks the validity of the swap, and exchanges the target nodes between the edges. For information on what values to select for directed, please see README.md.
Parameters
edge_list
:List
[Tuple
[int
,int
]]- Edge list representing the graph to be randomized. Tuples can contain
integer values representing nodes. No value should be greater than C++'s
INT_MAX
, in this case 2_147_483_647. allow_self_loops
:bool
- Whether to allow edges like (0, 0). In the case of bipartite graphs, such an edge represents a connection between two distinct nodes, while in other graphs it may represent an edge from a node to itself, in which case an edge may or may not be meaningful depending on context.
allow_antiparallel
:bool
- Whether to allow simultaneous edges like (0, 1) and (1, 0). In the case of bipartite graphs, these edges represent two connections between four distinct nodes, while for other graphs, these may be connections between the same two nodes.
multiplier
:float
- The number of edge swap attempts is determined by the product of the number of existing edges and multiplier. For example, if five edges are passed and multiplier is set to 10, 50 swaps will be attempted. Non-integer products will be rounded down to the nearest integer.
excluded_edges
:Set
[Tuple
[int
,int
]]- Specific edges which should never be created by the network randomization
seed
:int
- Random seed that will be passed to the C++ Mersenne Twister 19937 random number generator.
max_malloc
:int
(unsigned` `long` `long` `int
in
C
)- The maximum amount of memory to be allocated using
malloc
when making a bitset to hold edges. An uncompressed bitset is implemented for holding edges that is significantly faster than alternatives. However, it is memory-inefficient and will not be used if more memory is required thanmax_malloc
. Above the threshold, a Roaring bitset will be used.
Returns
new_edges
:List
[Tuple
[int
,int
]]- Edge list of a permutation of the network given as
edge_list
stats
:Dict
[str
,int
]- Information about the permutation performed. Gives the following information:
swap_attempts
- number of attempted swapssame_edge
- number of swaps rejected because one edge was chosen twiceself_loop
- number of swaps rejected because new edge is a self-loop 'duplicate- number of swaps rejected because new edge already exists
undir_duplicate- number of swaps rejected because the network is undirected and the reverse of the new edge already exists
excluded` - number of swaps rejected because new edge was among excluded
Source code
def permute_edge_list(edge_list: List[Tuple[int, int]], allow_self_loops: bool = False, allow_antiparallel: bool = False, multiplier: float = 10, excluded_edges: Set[Tuple[int, int]] = set(), seed: int = 0, max_malloc: int = 4000000000): """ Permute the edges of a graph using the XSwap method given by Hanhijärvi, et al. (doi.org/f3mn58). XSwap is a degree-preserving network randomization technique that selects edges, checks the validity of the swap, and exchanges the target nodes between the edges. For information on what values to select for directed, please see README.md. Parameters ---------- edge_list : List[Tuple[int, int]] Edge list representing the graph to be randomized. Tuples can contain integer values representing nodes. No value should be greater than C++'s `INT_MAX`, in this case 2_147_483_647. allow_self_loops : bool Whether to allow edges like (0, 0). In the case of bipartite graphs, such an edge represents a connection between two distinct nodes, while in other graphs it may represent an edge from a node to itself, in which case an edge may or may not be meaningful depending on context. allow_antiparallel : bool Whether to allow simultaneous edges like (0, 1) and (1, 0). In the case of bipartite graphs, these edges represent two connections between four distinct nodes, while for other graphs, these may be connections between the same two nodes. multiplier : float The number of edge swap attempts is determined by the product of the number of existing edges and multiplier. For example, if five edges are passed and multiplier is set to 10, 50 swaps will be attempted. Non-integer products will be rounded down to the nearest integer. excluded_edges : Set[Tuple[int, int]] Specific edges which should never be created by the network randomization seed : int Random seed that will be passed to the C++ Mersenne Twister 19937 random number generator. max_malloc : int (`unsigned long long int` in C) The maximum amount of memory to be allocated using `malloc` when making a bitset to hold edges. An uncompressed bitset is implemented for holding edges that is significantly faster than alternatives. However, it is memory-inefficient and will not be used if more memory is required than `max_malloc`. Above the threshold, a Roaring bitset will be used. Returns ------- new_edges : List[Tuple[int, int]] Edge list of a permutation of the network given as `edge_list` stats : Dict[str, int] Information about the permutation performed. Gives the following information: `swap_attempts` - number of attempted swaps `same_edge` - number of swaps rejected because one edge was chosen twice `self_loop` - number of swaps rejected because new edge is a self-loop 'duplicate` - number of swaps rejected because new edge already exists `undir_duplicate` - number of swaps rejected because the network is undirected and the reverse of the new edge already exists `excluded` - number of swaps rejected because new edge was among excluded """ import xswap._xswap_backend if len(edge_list) != len(set(edge_list)): raise ValueError("Edge list contained duplicate edges.") # Number of attempted XSwap swaps num_swaps = int(multiplier * len(edge_list)) # Compute the maximum node ID (for creating the bitset) max_id = max(map(max, edge_list)) new_edges, stats = xswap._xswap_backend._xswap( edge_list, list(excluded_edges), max_id, allow_self_loops, allow_antiparallel, num_swaps, seed, max_malloc) return new_edges, stats