def eva(g, labels, weight='weight', resolution=1., randomize=False, alpha=0.5):
"""
The Eva algorithm extends the Louvain approach in order to deal with the attributes of the nodes (aka Louvain Extended to Vertex Attributes).
It optimizes - combining them linearly - two quality functions, a structural and a clustering one, namely Newman's modularity and purity, estimated as the product of the frequencies of the most frequent labels carried by the nodes within the communities.
A parameter alpha tunes the importance of the two functions: an high value of alpha favors the clustering criterion instead of the structural one.
:param g: a networkx/igraph object
:param labels: dictionary specifying for each node (key) a dict (value) specifying the name attribute (key) and its value (value)
:param weight: str, optional the key in graph to use as weight. Default to 'weight'
:param resolution: double, optional Will change the size of the communities, default to 1.
:param randomize: boolean, optional Will randomize the node evaluation order and the community evaluation order to get different partitions at each call, default False
:param alpha: float, assumed in [0,1], optional Will tune the importance of modularity and purity criteria, default to 0.5
:return: AttrNodeClustering object
:Example:
>>> from cdlib.algorithms import eva
>>> import networkx as nx
>>> import random
>>> l1 = ['A', 'B', 'C', 'D']
>>> l2 = ["E", "F", "G"]
>>> g_attr = nx.barabasi_albert_graph(100, 5)
>>> labels=dict()
>>> for node in g_attr.nodes():
>>> labels[node]={"l1":random.choice(l1), "l2":random.choice(l2)}
>>> communities = eva(g_attr, labels, alpha=0.8)
:References:
1. Citraro, S., & Rossetti, G. (2019, December). Eva: Attribute-Aware Network Segmentation. In International Conference on Complex Networks and Their Applications (pp. 141-151). Springer, Cham.
.. note:: Reference implementation: https://github.com/GiulioRossetti/Eva/tree/master/Eva
"""
g = convert_graph_formats(g, nx.Graph)
nx.set_node_attributes(g, labels)
coms, coms_labels = Eva.eva_best_partition(g,
weight=weight,
resolution=resolution,
randomize=randomize,
alpha=alpha)
# Reshaping the results
coms_to_node = defaultdict(list)
for n, c in coms.items():
coms_to_node[c].append(n)
coms_eva = [list(c) for c in coms_to_node.values()]
return AttrNodeClustering(coms_eva,
g,
"Eva",
coms_labels,
method_parameters={
"weight": weight,
"resolution": resolution,
"randomize": randomize,
"alpha": alpha
})
def ilouvain(g_original: object, labels: dict) -> AttrNodeClustering:
"""
The I-Louvain algorithm extends the Louvain approach in order to deal only with the scalar attributes of the nodes.
It optimizes Newman's modularity combined with an entropy measure.
**Supported Graph Types**
========== ======== ======== ======== ==============
Undirected Directed Weighted Temporal Node Attribute
========== ======== ======== ======== ==============
Yes No No No Yes
========== ======== ======== ======== ==============
:param g_original: a networkx/igraph object
:param labels: dictionary specifying for each node (key) a dict (value) specifying the name attribute (key) and its value (value)
:return: AttrNodeClustering object
:Example:
>>> from cdlib.algorithms import ilouvain
>>> import networkx as nx
>>> import random
>>> l1 = [0.1, 0.4, 0.5]
>>> l2 = [34, 3, 112]
>>> g_attr = nx.barabasi_albert_graph(100, 5)
>>> labels=dict()
>>> for node in g_attr.nodes():
>>> labels[node]={"l1":random.choice(l1), "l2":random.choice(l2)}
>>> id = dict()
>>> for n in g.nodes():
>>> id[n] = n
>>> communities = ilouvain(g_attr, labels, id)
:References:
Combe D., Largeron C., Géry M., Egyed-Zsigmond E. "I-Louvain: An Attributed Graph Clustering Method". <https://link.springer.com/chapter/10.1007/978-3-319-24465-5_16> In: Fromont E., De Bie T., van Leeuwen M. (eds) Advances in Intelligent Data Analysis XIV. IDA (2015). Lecture Notes in Computer Science, vol 9385. Springer, Cham
"""
g = convert_graph_formats(g_original, nx.Graph)
nx.set_node_attributes(g, labels)
nid = dict()
for n in g.nodes():
nid[n] = n
algo = ML2(g, labels, nid)
coms = algo.findPartition()
# Reshaping the results
coms_to_node = defaultdict(list)
for n, c in coms.items():
coms_to_node[c].append(n)
coms_ilouv = [list(c) for c in coms_to_node.values()]
return AttrNodeClustering(coms_ilouv, g_original, "ILouvain")