def __init__(self, model, logger, interval, mute_threshold):
Demon.__init__(
self,
self.monitor_task, self.monitor_init, self.monitor_term,
interval=interval, head_start=True)
self.model = model
self.logger = logger
self.mute_threshold = mute_threshold
def __init__(self, model, logger, interval, mute_threshold):
Demon.__init__(self,
self.monitor_task,
self.monitor_init,
self.monitor_term,
interval=interval,
head_start=True)
self.model = model
self.logger = logger
self.mute_threshold = mute_threshold
class FinalLevel():
def __init__(self):
self.screen = Screen(100, 30)
self.paddle = Paddle(15, self.screen)
self.ball = Ball(self.screen, self.paddle, False)
self.paddle.place(self.paddle.size)
self.ball.move()
def placeDemon(self):
self.demon = Demon(self.screen, self.paddle, self.ball)
self.screen.setDemon(self.demon, self.paddle)
self.ball.demon(self.demon)
self.demon.place()
def demon(g_original, epsilon, min_com_size=3):
"""
Demon is a node-centric bottom-up overlapping community discovery algorithm.
It leverages ego-network structures and overlapping label propagation to identify micro-scale communities that are subsequently merged in mesoscale ones.
:param g_original: a networkx/igraph object
:param epsilon: merging threshold in [0,1], default 0.25.
:param min_com_size: minimum community size, default 3.
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.demon(G, min_com_size=3, epsilon=0.25)
:References:
1. Coscia, M., Rossetti, G., Giannotti, F., & Pedreschi, D. (2012, August). `Demon: a local-first discovery method for overlapping communities. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.721.1788&rep=rep1&type=pdf/>`_ In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 615-623). ACM.
2. Coscia, M., Rossetti, G., Giannotti, F., & Pedreschi, D. (2014). `Uncovering hierarchical and overlapping communities with a local-first approach. <https://dl.acm.org/citation.cfm?id=2629511/>`_ ACM Transactions on Knowledge Discovery from Data (TKDD), 9(1), 6.
.. note:: Reference implementation: https://github.com/GiulioRossetti/DEMON
"""
g = convert_graph_formats(g_original, nx.Graph)
with suppress_stdout():
dm = Demon(graph=g, epsilon=epsilon, min_community_size=min_com_size)
coms = dm.execute()
coms = [list(c) for c in coms]
return NodeClustering(coms,
g_original,
"DEMON",
method_parameters={
"epsilon": epsilon,
"min_com_size": min_com_size
},
overlap=True)
def placeDemon(self):
self.demon = Demon(self.screen, self.paddle, self.ball)
self.screen.setDemon(self.demon, self.paddle)
self.ball.demon(self.demon)
self.demon.place()