diff --git a/generate_theoric_random_graph.py b/generate_theoric_random_graph.py index c9d9425c8230acbcbe28d827222fb04a3ab2c645..ab866b742c31eda2ce7a758abfa8a9b301eba3c0 100644 --- a/generate_theoric_random_graph.py +++ b/generate_theoric_random_graph.py @@ -43,7 +43,7 @@ parameters = { }, "fast_gnp_random_graph": { "n": GRAPH_SIZE, - "p": [0.1,0.4,0.6] + "p": [0.4,0.6] }, "random_powerlaw_tree_sequence": { # configuration_model "n": GRAPH_SIZE, @@ -55,12 +55,12 @@ parameters = { }, "waxman_graph": { "n": GRAPH_SIZE, - "beta": [0.1,0.4,0.6], - "alpha": [0.1,0.4,0.6] + "beta": [0.4,0.6], + "alpha": [0.4,0.6] }, "geographical_threshold_graph": { "n": GRAPH_SIZE, - "theta": [0.1,0.2,0.3] + "theta": [0.2,0.3] }, } # Generating transition matrices for stochastic block model @@ -96,28 +96,3 @@ for method,args in pbar: # print(e) -""" - -nx.planted_partition_graph(5, 5, 0.8, - 0.1) # nb of blocks, nb of vertices in each block, probability of link in block , prob. of link between blocks -nx.stochastic_block_model([5, 3, 6], - [[1, 0.1, 0.1], [0.1, 1, 0.1], [0.1, 0.1, 1]]) # sizes of blocks,probability between blocks - -## Random Graph - -nx.fast_gnp_random_graph(10, 0.5) # nb of nodes, prob of link -sequence = nx.random_powerlaw_tree_sequence(50, tries=500) # degree sequence -G = nx.configuration_model(sequence) # sequence - -## Spatial graph - -nx.random_geometric_graph(20, 0.5) # size of graph, max radius between edge connected - -dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y)) # euclidian distance -G = nx.waxman_graph(50, 0.5, 0.1, metric=dist) # nb of nodes, beta, alpha -# beta -> the higher the parameter the higher the density is -# alpha -> the lower the higher the number of short edge is - -G = nx.geographical_threshold_graph(20, 1, metric=dist) # nb of node, theta -# theta --> distance thresold -""" \ No newline at end of file