Fixes after code review

nx_parallel/algorithms/centrality/betweenness.py

@@ -97,14 +97,14 @@ def edge_betweenness_centrality(
     """The parallel computation is implemented by dividing the nodes into chunks and
         computing edge betweenness centrality for each chunk concurrently.
 
+    networkx.edge_betweenness_centrality : https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.edge_betweenness_centrality.html
+
     Parameters
-    ------------
+    ----------
     get_chunks : str, function (default = "chunks")
         A function that takes in a list of all the nodes as input and returns an
         iterable `node_chunks`. The default chunking is done by slicing the
         `nodes` into `n` chunks, where `n` is the number of CPU cores.
-
-    networkx.edge_betweenness_centrality : https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.edge_betweenness_centrality.html
     """
     if hasattr(G, "graph_object"):
         G = G.graph_object

nx_parallel/algorithms/centrality/tests/test_betweenness_centrality.py

@@ -26,14 +26,6 @@ def get_chunk(nodes):
     return get_chunk
 
 
-def get_chunk_edges_random(nodes):
-    num_chunks = nxp.cpu_count()
-    chunks = [[] for _ in range(num_chunks)]
-    for i, node in enumerate(nodes):
-        chunks[i % num_chunks].append(node)
-    return chunks
-
-
 def test_betweenness_centrality_get_chunks():
     G = nx.fast_gnp_random_graph(100, 0.1, directed=False)
     H = nxp.ParallelGraph(G)
@@ -47,17 +39,3 @@ def test_betweenness_centrality_get_chunks():
         assert math.isclose(par_bc[i], par_bc_chunk[i], abs_tol=1e-16)
     # get_chunk is faster than default(for big graphs)
     # G = nx.bipartite.random_graph(400, 700, 0.8, seed=5, directed=False)
-
-
-def test_edge_betweenness_centrality_get_chunks():
-    G = nx.fast_gnp_random_graph(100, 0.1, directed=False)
-    H = nxp.ParallelGraph(G)
-    par_bc_chunk = nxp.edge_betweenness_centrality(
-        H, get_chunks=get_chunk_edges_random
-    )  # smoke test
-    par_bc = nxp.edge_betweenness_centrality(H)
-
-    for e in G.edges:
-        assert math.isclose(par_bc[e], par_bc_chunk[e], abs_tol=1e-16)
-    # get_chunk is faster than default(for big graphs)
-    # G = nx.bipartite.random_graph(400, 700, 0.8, seed=5, directed=False)

timing/timing_all_functions.py

@@ -9,19 +9,13 @@
 
 # Code to create README heatmap for all functions in function_list
 heatmapDF = pd.DataFrame()
-function_list = [
-    nx.betweenness_centrality,
-    nx.edge_betweenness_centrality,
-    nx.closeness_vitality,
-    nx.local_efficiency,
-]
+function_list = [nx.betweenness_centrality, nx.closeness_vitality, nx.local_efficiency]
 number_of_nodes_list = [10, 20, 50, 300, 600]
 
 for i in range(0, len(function_list)):
     currFun = function_list[i]
     for j in range(0, len(number_of_nodes_list)):
         num = number_of_nodes_list[j]
-        print(f"Starting {currFun} with {num} nodes")
 
         # create original and parallel graphs
         G = nx.fast_gnp_random_graph(num, 0.5, directed=False)
@@ -44,7 +38,7 @@
 for j in range(0, len(number_of_nodes_list)):
     num = number_of_nodes_list[j]
     G = nx.tournament.random_tournament(num)
-    H = nx_parallel.ParallelGraph(G)
+    H = nx_parallel.ParallelDiGraph(G)
     t1 = time.time()
     c = nx.tournament.is_reachable(H, 1, num)
     t2 = time.time()
@@ -64,7 +58,6 @@
 hm.set_yticklabels(
     [
         "betweenness_centrality",
-        "edge_betweenness_centrality",
         "closeness_vitality",
         "local_efficiency",
         "tournament is_reachable",