Source code for

"""Asynchronous Fluid Communities algorithm for community detection."""

from collections import Counter

import networkx as nx
from networkx.algorithms.components import is_connected
from networkx.exception import NetworkXError
from networkx.utils import groups, not_implemented_for, py_random_state

__all__ = ["asyn_fluidc"]

[docs] @not_implemented_for("directed") @not_implemented_for("multigraph") @py_random_state(3) @nx._dispatchable def asyn_fluidc(G, k, max_iter=100, seed=None): """Returns communities in `G` as detected by Fluid Communities algorithm. The asynchronous fluid communities algorithm is described in [1]_. The algorithm is based on the simple idea of fluids interacting in an environment, expanding and pushing each other. Its initialization is random, so found communities may vary on different executions. The algorithm proceeds as follows. First each of the initial k communities is initialized in a random vertex in the graph. Then the algorithm iterates over all vertices in a random order, updating the community of each vertex based on its own community and the communities of its neighbors. This process is performed several times until convergence. At all times, each community has a total density of 1, which is equally distributed among the vertices it contains. If a vertex changes of community, vertex densities of affected communities are adjusted immediately. When a complete iteration over all vertices is done, such that no vertex changes the community it belongs to, the algorithm has converged and returns. This is the original version of the algorithm described in [1]_. Unfortunately, it does not support weighted graphs yet. Parameters ---------- G : NetworkX graph Graph must be simple and undirected. k : integer The number of communities to be found. max_iter : integer The number of maximum iterations allowed. By default 100. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- communities : iterable Iterable of communities given as sets of nodes. Notes ----- k variable is not an optional argument. References ---------- .. [1] Parés F., Garcia-Gasulla D. et al. "Fluid Communities: A Competitive and Highly Scalable Community Detection Algorithm". []. """ # Initial checks if not isinstance(k, int): raise NetworkXError("k must be an integer.") if not k > 0: raise NetworkXError("k must be greater than 0.") if not is_connected(G): raise NetworkXError("Fluid Communities require connected Graphs.") if len(G) < k: raise NetworkXError("k cannot be bigger than the number of nodes.") # Initialization max_density = 1.0 vertices = list(G) seed.shuffle(vertices) communities = {n: i for i, n in enumerate(vertices[:k])} density = {} com_to_numvertices = {} for vertex in communities: com_to_numvertices[communities[vertex]] = 1 density[communities[vertex]] = max_density # Set up control variables and start iterating iter_count = 0 cont = True while cont: cont = False iter_count += 1 # Loop over all vertices in graph in a random order vertices = list(G) seed.shuffle(vertices) for vertex in vertices: # Updating rule com_counter = Counter() # Take into account self vertex community try: com_counter.update({communities[vertex]: density[communities[vertex]]}) except KeyError: pass # Gather neighbor vertex communities for v in G[vertex]: try: com_counter.update({communities[v]: density[communities[v]]}) except KeyError: continue # Check which is the community with highest density new_com = -1 if len(com_counter.keys()) > 0: max_freq = max(com_counter.values()) best_communities = [ com for com, freq in com_counter.items() if (max_freq - freq) < 0.0001 ] # If actual vertex com in best communities, it is preserved try: if communities[vertex] in best_communities: new_com = communities[vertex] except KeyError: pass # If vertex community changes... if new_com == -1: # Set flag of non-convergence cont = True # Randomly chose a new community from candidates new_com = seed.choice(best_communities) # Update previous community status try: com_to_numvertices[communities[vertex]] -= 1 density[communities[vertex]] = ( max_density / com_to_numvertices[communities[vertex]] ) except KeyError: pass # Update new community status communities[vertex] = new_com com_to_numvertices[communities[vertex]] += 1 density[communities[vertex]] = ( max_density / com_to_numvertices[communities[vertex]] ) # If maximum iterations reached --> output actual results if iter_count > max_iter: break # Return results by grouping communities as list of vertices return iter(groups(communities).values())