Adding the GLANB Filter

netbone/statistical/glanb.py

+import networkx as nx
+import igraph as ig
+import pandas as pd
+from netbone.backbone import Backbone
+from netbone.filters import threshold_filter, fraction_filter
+
+
+def count_included_subarrays(arrays, target_array):
+    count = 0
+    target_len = len(target_array)
+    for array in arrays:
+        array_len = len(array)
+        for i in range(array_len - target_len + 1):
+            if array[i:i + target_len] == target_array:
+                count += 1
+    return count
+
+
+def glanb(data, **kwargs):
+    if isinstance(data, pd.DataFrame):
+        graph = nx.from_pandas_edgelist(data, edge_attr='weight', create_using=nx.Graph())
+    elif isinstance(data, nx.Graph):
+        graph = data.copy()
+    else:
+        print("data should be a panads dataframe or nx graph")
+        return
+    c = -1
+    for key, value in kwargs.items():
+        if key == 'c':
+            c = int(value)
+    if c == -1:
+        print("Please send the c value")
+        return
+    # convert weights to distances
+    wes = nx.get_edge_attributes(graph, 'weight')
+    values = {pair: 1 / wes[pair] for pair in wes}
+    nx.set_edge_attributes(graph, values, name='distance')
+
+    node_labels = dict(zip(graph.nodes(), range(len(graph))))
+    igraph = ig.Graph.from_networkx(graph)
+    for source in graph.nodes():
+        k_i = graph.degree[source]
+        if k_i > 1:
+            ig_paths = igraph.get_all_shortest_paths(node_labels[source], weights='distance')
+            for u, v in graph.edges(source):
+                g_ij = count_included_subarrays(ig_paths, [node_labels[u], node_labels[v]])
+                g_is = len(ig_paths) - 1
+                I_ij = (g_ij / g_is)
+                S_ij = (1 - I_ij) ** ((k_i - 1) ** c)
+                if 'SI' in graph[u][v]:
+                    if S_ij < graph[u][v]['SI']:
+                        graph[u][v]['SI'] = S_ij
+                else:
+                    graph[u][v]['SI'] = S_ij
+    return Backbone(graph, method_name="Globally and Locally Adaptive Backbone Filter", property_name="SI",
+                    ascending=True, compatible_filters=[threshold_filter, fraction_filter], filter_on='Edges')