Community events

cdlib/__init__.py

@@ -1,8 +1,9 @@
-__version__ = '0.3.1'
+__version__ = "0.4.0"
 from cdlib.classes.node_clustering import NodeClustering
 from cdlib.classes.edge_clustering import EdgeClustering
 from cdlib.classes.fuzzy_node_clustering import FuzzyNodeClustering
 from cdlib.classes.attr_node_clustering import AttrNodeClustering
 from cdlib.classes.bipartite_node_clustering import BiNodeClustering
 from cdlib.classes.temporal_clustering import TemporalClustering
 from cdlib.classes.named_clustering import NamedClustering
+from cdlib.lifecycles import LifeCycle, CommunityEvent

cdlib/algorithms/crisp_partition.py

@@ -517,13 +517,12 @@ def louvain(
     ========== ======== ========
 
     :param g_original: a networkx/igraph object
-    :param partition : NodeClustering object, optional the algorithm will start using this partition of the nodes.
-    :param weight: str, optional the key in graph to use as weight. Default to 'weight'
+    :param partition: NodeClustering object, optional the algorithm will start using this partition of the nodes
+    :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: int, RandomState instance or None, optional (default=None). If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
+    :param randomize: int, RandomState instance or None, optional (default=None).
     :return: NodeClustering object
 
-
     :Example:
 
     >>> from cdlib import algorithms
@@ -536,6 +535,7 @@ def louvain(
     Blondel, Vincent D., et al. `Fast unfolding of communities in large networks. <https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/meta/>`_ Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.
 
     .. note:: Reference implementation: https://github.com/taynaud/python-louvain
+
     """
 
     g = convert_graph_formats(g_original, nx.Graph)
@@ -2689,9 +2689,21 @@ def paris(g_original: object) -> NodeClustering:
 
     .. note:: Reference implementation: https://github.com/tbonald/paris
     """
+
     g = convert_graph_formats(g_original, nx.Graph)
-    D = paris_alg(g)
-    clustering = paris_best_clustering(D)
+
+    dmap = {n: i for i, n in enumerate(g.nodes)}
+    reverse_map = {i: n for n, i in dmap.items()}
+    nx.relabel_nodes(g_original, dmap, False)
+
+    D = paris_alg(g_original)
+    coms = paris_best_clustering(D)
+
+    clustering = []
+
+    for com in coms:
+        com = [reverse_map[c] for c in com]
+        clustering.append(com)
 
     return NodeClustering(
         clustering, g_original, "Paris", method_parameters={}, overlap=False

cdlib/algorithms/temporal_partition.py

@@ -1,5 +1,6 @@
 from cdlib import TemporalClustering, NamedClustering
 from cdlib.algorithms.internal_dcd.eTILES import eTILES
+import networkx as nx
 
 __all__ = ["tiles"]
 
@@ -34,7 +35,7 @@ def tiles(dg: object, obs: int = 1) -> TemporalClustering:
 
     :References:
 
-    Rossetti, Giulio; Pappalardo, Luca; Pedreschi, Dino, and Giannotti, Fosca. `Tiles: an online algorithm for community discovery in dynamic social networks.<https://link.springer.com/article/10.1007/s10994-016-5582-8>`_ Machine Learning (2016), 106(8), 1213-1241.
+    Rossetti, Giulio; Pappalardo, Luca; Pedreschi, Dino, and Giannotti, Fosca. Tiles: an online algorithm for community discovery in dynamic social networks. Machine Learning (2016), 106(8), 1213-1241.
     """
     alg = eTILES(dg=dg, obs=obs)
     tc = TemporalClustering()
@@ -57,8 +58,10 @@ def tiles(dg: object, obs: int = 1) -> TemporalClustering:
     mtc = alg.get_matches()
     tc.add_matching(mtc)
 
+    ### polytree
+
     # cleaning & updating community matching
-    dg = tc.lifecycle_polytree(None, False)
+    dg = __lifecycle_polytree(tc)
     community_ids = list(dg.nodes())
 
     tids = tc.get_observation_ids()
@@ -77,3 +80,22 @@ def tiles(dg: object, obs: int = 1) -> TemporalClustering:
     tc.add_matching(mtc)
 
     return tc
+
+
+def __lifecycle_polytree(tc) -> nx.DiGraph:
+    """
+    Reconstruct the poly-tree representing communities lifecycles using a provided similarity function.
+    """
+
+    lifecycle = tc.matching
+
+    pt = nx.DiGraph()
+    if len(lifecycle[0]) == 3:
+        for u, v, w in lifecycle:
+            pt.add_edge(u, v, weight=w)
+    else:
+        # implicit matching
+        for u, v in lifecycle:
+            pt.add_edge(u, v)
+
+    return pt